AGENTIC

ARTIFICIAL INTELLIGENCE
Harnessing AI Agents to Reinvent
Business, Work, and Life

PASCAL BORNET
Jochen Wirtz – Thomas H. Davenport
David De Cremer – Brian Evergreen
Phil Fersht – Rakesh Gohel – Shail Khiyara
Key Contributors to Agentic Artificial
Intelligence
This book is the result of a unique collaboration among some of
the brightest minds in agentic AI—a field that is rapidly reshaping
technology and business. The contributors to this book come
from diverse backgrounds, including AI researchers, business
executives, high-level developers, and hands-on consultants who
have implemented AI agents across industries worldwide. Their
collective expertise, spanning deep technical knowledge, real-
world implementation experience, and strategic business insights,
has been essential in shaping this book’s depth and vision.
Below, the contributors are listed in alphabetical order by
last name:

• Ian Barkin • Maxim Ioffe

• Pierre Louis Bouchard • Nandan Mullakara
• Nicholas Cravino • Arnaud Morvan
• Dana Daher • Ramnath Natarajan
• Simon Ellis • Jan Oberhauser
• Andy Fanning • Lasse Rindom
• Olivier Gomez • Toran Bruce Richards
• Kieran Gilmurray • Sharbs Shaaya
• Mohsin Khan • Pooja Sund
• Cassie Kozyrkov

Each of these individuals has brought unique perspectives,

technical depth, and practical expertise to this book, helping to
explore not just what AI agents are, but how they are being built,
deployed, and scaled in the real world. To all of you—thank you
for your invaluable contributions.
Published by
World Scientific Publishing Co. Pte. Ltd.
5 Toh Tuck Link, Singapore 596224
USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601
UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

National Library Board, Singapore Cataloguing in Publication Data

Name(s): Bornet, Pascal. | Wirtz, Jochen, author.
Title: Agentic artificial intelligence : harnessing AI agents to reinvent business, work, and life /
Pascal Bornet, Jochen Wirtz [and 6 others].
Description: Singapore : World Scientific Publishing Co. Pte. Ltd., [2025]
Identifier(s): ISBN 978-981-98-1566-1 (hardcover) | ISBN 978-981-98-1622-4 (paperback) |
ISBN 978-981-98-1567-8 (ebook for institutions) |
ISBN 978-981-98-1568-5 (ebook for individuals)
Subject(s): LCSH: Artificial intelligence. | Artificial intelligence--Industrial applications. |
Artificial intelligence--Social aspects.
Classification: DDC 006.3--dc23

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library.

Copyright © 2025 by Pascal Bornet and Jochen Wirtz

All rights reserved.

For any available supplementary material, please visit

https://www.worldscientific.com/worldscibooks/10.1142/14380#t=suppl

Desk Editor: Geysilla Jean Ortiz

Design and layout: Lionel Seow

Printed in Singapore
“Agents are (…) bringing about the biggest revolution in
computing since we went from typing commands to tapping on
icons.” — Bill Gates

“AI agents will become the primary way we interact with

computers in the future.” — Satya Nadella

“The age of agentic AI is here.”— Jensen Huang

We dedicate this book to our children
and to all the children in the world.
We owe them the best future.
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CONTENTS

PREFACE: A Journey Toward Human Potential..............xiii

INTRODUCTION...................................................................1
The Promise (and Limitations) of AI Agents.................20
What You Will Learn from the Book.............................25
Beyond the Book: Your Online Resources.....................30
Key Terminologies for Understanding AI Agents..........31

PART 1: THE RISE OF AI AGENTS..................................33

CHAPTER 1: Beyond ChatGPT: The Next
Evolution of AI.......................................................................35
The Birth of Agentic AI: A Convergence of Powers......35
Agentic AI for Entrepreneurship and Business..............47
The State of AI Agent Adoption in Companies..............54
CHAPTER 2: The Five Levels of AI Agents: From
Automation to Autonomy......................................................61
Breaking Down the AI Agent’s Capabilities..................61
The Complex Reality of AI Agents’ Capabilities...........67
The Agentic AI Progression Framework........................69
The Magic of Progressive Autonomy:
Understanding AI Agent Levels.....................................77

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 Agentic Artificial Intelligence

CHAPTER 3: Inside the Mind of an AI Agent....................83

Key Specificities of AI Agents.......................................83
Inherent Limitations of AI Agents..................................87
When One Is Not Enough: The Power and
Practice of Multi-Agent Systems...................................90
The Agent’s Dilemma: Balancing Creativity
with Reliability.............................................................100
CHAPTER 4: Putting AI Agents to the Test......................109
Digital Hands: When AI Learned to Use Computers... 110
Our First Steps with a “Computer Use”
AI Agent: The Invoice Test.......................................... 112
When AI Meets the Paperclip Challenge..................... 114
The outcome of the experiment.................................... 118
Lessons learned from the experiments.........................121

PART 2: THE THREE KEYSTONES OF AGENTIC AI... 125

CHAPTER 5: Action: Teaching AI to Do, Not
Just Think.............................................................................129
The Detective’s Dilemma.............................................130
Tools as Building Blocks..............................................133
Inside the AI Agent’s Toolkit........................................139
From Basic to Advanced Tool Usage...........................144
When Tools Meet Trust................................................154
CHAPTER 6: Reasoning: From Fast to Wise...................165
AI Reasoning: Introducing The Power of Pause..........167
The Power of Many: Multi-Agent Systems
in AI Reasoning............................................................185
CHAPTER 7: Memory: Building AI That Learns............197
Memory is a Foundation of Intelligence......................198
The Intricate Dance of Short-Term Memory
in AI Agents..................................................................206
The Power of Long-Term Memory:
Transforming AI from Tools to Partners......................216

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 Contents

Designing and Implementing Long-Term Memory in

Agentic AI Systems......................................................219
Adaptation and Learning through Feedback Loops.....229
Best Practices in Managing Memory for Agents.........243

PART 3: ENTREPRENEURSHIP AND

PROFESSIONAL GROWTH WITH AI AGENTS..........253
CHAPTER 8: A Practical Guide For Building
Successful AI Agents............................................................255
Step 1: Finding the Right Agentic Opportunities.........256
Step 2: Defining AI Agents’ Role and Capabilities......270
Step 3: Designing AI Agents for Success.....................276
Step 4: Implementing Your AI Agents.........................284
CHAPTER 9: From Ideas to Income: Business
Models for the Agent Economy...........................................323
The Birth of Self-Running Businesses: When
AI Became an Entrepreneur.........................................323
Emerging Business Models in the Age of
Agentic AI....................................................................329
Building Opportunities in the Agentic
AI Economy: The New App Gold Rush.......................343

PART 4: ENTERPRISE TRANSFORMATION

THROUGH AGENTIC AI..................................................355
CHAPTER 10: Human–Agent Collaboration:
Leadership, Trust, and Change..........................................357
Mastering Work Design and Change Management
at Scale.........................................................................357
Leadership in the Age of AI Agents: Building
Trust and Collaboration in Hybrid Teams....................375
The Foundation: Management Vision and
Governance...................................................................389

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 Agentic Artificial Intelligence

CHAPTER 11: Scaling AI Agents: From Vision

to Reality...............................................................................403
The Right Scaling Approach........................................403
The Automation Experience Advantage: from
Level 2 to Level 3 agents.............................................410
Leveraging Generative AI and AI Agents for
a Holistic AI Corporate Transformation.......................415
When Agents Go Rogue: Building Essential
Safeguards for AI Systems...........................................421
CHAPTER 12: Case Study and Use Cases of
Agents Across Industries.....................................................427
Case Study: Pioneering Enterprise AI Agent
Transformation: Pets at Home......................................428
Agentic Use Cases Across Functions and Industries...438

PART 5: FUTURE HORIZONS FOR WORK

AND SOCIETY....................................................................441
CHAPTER 13: The New World of Work...........................443
Work Reimagined: The Symphony of Human
and Machine.................................................................443
This Time Is Different: The Dawn of Agentic AI.........453
Reinventing Education in the Age of AI Agents..........456
CHAPTER 14: Society in the Age of Agents.....................463
Reimagining Human Potential in an
Agent-Powered World..................................................463
A Framework for Governing the Future of Agentic AI.... 471
CONCLUSION....................................................................479
The Next Horizon: Emerging Capabilities...................480
The Urgency of AI Governance: Building
Guardrails Before It’s Too Late....................................482
Reflection and Broader Implications............................484
Your Action Plan..........................................................485
The Power of Choice....................................................488

x
 Contents

More Resources on Agentic AI............................................491

About the Authors................................................................493

APPENDICES: Practical Resources..................................501

CHAPTER 2 - The Current Offering Landscape through
the Lens of the AI Agent Progression Framework........... 502
CHAPTER 8 - Example of an AI Agent Identity:
Our Newsletter Summarization Agent.........................503
CHAPTER 8 - Example of Error Handling
Procedures for our Newsletter Project Agents.............508
CHAPTER 8 - Example of Implementation of
an Agent Using a Low-Code Platform.........................510
CHAPTER 12 – Use Cases: Enterprise AI
Agent Application........................................................531
CHAPTER 12 – Use Cases: Personal Productivity
AI Agent Applications..................................................545

INDEX...................................................................................551

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PREFACE: A JOURNEY
TOWARD HUMAN
POTENTIAL

T
here’s a profound transformation happening in how we
work, live, and create value. While many see this as a
purely technological revolution, we see something far
more meaningful: an opportunity to redefine the relationship
between humans and machines in ways that amplify what makes
us uniquely human.
We are a diverse team of twenty-seven professionals spanning
business, academia, programming, and research, united by a shared
vision of how technology can serve humanity. Our backgrounds
range from implementing enterprise-scale automation systems
to pioneering research in artificial intelligence, consulting with
Fortune 500 companies, and studying the societal implications
of technological change. What brings us together isn’t just our
expertise—it’s our shared belief that technology should enhance
human potential rather than replace it.
Our journey to this book began years ago, though we didn’t
know it at the time. Many of us were among the first to implement
intelligent automation systems in major organizations worldwide.
We pioneered approaches to combine artificial intelligence with
robotic process automation (RPA), creating systems that could
handle increasingly complex end-to-end business processes.

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 Agentic Artificial Intelligence

This work led some of us to co-author Intelligent Automation in

2020,1 which became a global bestseller and helped organizations
rethink their approach to digital transformation.
We didn’t realize then that we were laying the groundwork for
something even more transformative. The intelligent automation
systems we built over the past fifteen years—which combine
process automation with artificial intelligence to handle structured
workflows—have become the foundation for today’s agentic
systems. The progression makes perfect sense: before a system can
act autonomously (as agents do), it needs to master the basics of
executing processes, handling data, and making decisions within
defined parameters. These are exactly the capabilities we’ve spent
years refining in intelligent automation systems.
This foundation gave us a unique advantage when the latest
breakthroughs in generative AI opened the door to modern
agentic systems. We had already gained experience with many of
the fundamental challenges: how to reliably automate complex
processes, how to handle exceptions gracefully, how to integrate
with existing systems, and most importantly, how to implement
these technologies in ways that enhance rather than replace
human capabilities. When companies began exploring agentic
systems a few years ago, many naturally evolved from their
existing intelligent automation platforms, building upon these
proven foundations to create more sophisticated, autonomous
capabilities.
Yet, we approach this topic with humility. Despite our
collective experience—or perhaps because of it—we recognize
that we’re all still learning. The field is evolving rapidly, and new
possibilities emerge almost daily. What makes our contribution
unique is not just our technical or business expertise but also our

1
Pascal Bornet, Ian Barkin, and Jochen Wirtz, 2020. “INTELLIGENT
AUTOMATION: Learn how to harness Artificial Intelligence to boost business
& make our world more human”. https://www.amazon.com/INTELLIGENT-
AUTOMATION-Artificial-Intelligence-business/dp/B08KTDVHHQ

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 PREFACE: A Journey Toward Human Potential

understanding of how to implement these technologies in ways

that serve human flourishing.
Our goal isn’t just to explain new technology—we want to
give people and businesses the tools to build a better world. A
world where workers have more meaningful jobs and a better
work-life balance, where companies operate more efficiently
while delivering exceptional customer experiences. A world
where healthcare systems save more lives through smarter
care coordination and schools provide personalized, effective
learning for every student. A world where communities can
solve complex challenges by using resources more intelligently.
AI isn’t just about automation—it’s about creating real impact
where it matters most.
This book is written for leaders, professionals, entrepreneurs,
and curious minds who sense the magnitude of the changes
ahead and want to understand how to navigate them. So,
whether you’re a business executive looking to transform your
organization, a professional wondering about the future of your
career, or simply someone interested in how technology will
reshape our world, we wrote this book for you.
We believe we’re at a pivotal moment in history—one where
the decisions we make about how to implement and direct these
technologies will have far-reaching implications for generations
to come.
Through these pages, we’ll share what we’ve learned from
our successes and failures, the patterns we’ve observed across
industries, and the principles we believe will be crucial for
thriving in this new era.
Let’s embark on this exploration together, guided not just by
technological possibility, but by a vision of what technology can
help us become.

—The Authors
March 2025

xv
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INTRODUCTION

Are We Missing the Point

with Generative AI?

P
icture this: Your competitor just announced they’re running
their entire operation with a team one-fifth the size of yours,
yet they’re growing twice as fast. Their secret? They’ve
deployed AI agents that autonomously handle everything from
customer service to operations, achieving in hours what takes your
team weeks.
Sounds far-fetched? It’s happening right now. Let us be
provocative here. While most businesses are still figuring out
how to use ChatGPT for writing emails and creating chatbots, a
new breed of organizations is fundamentally reimagining what’s
possible with AI. They’re not just automating tasks—they’re
creating self-operating businesses that scale effortlessly, adapt
continuously, and never sleep.
But here’s the paradox that’s holding most organizations
back: We’ve built generative AI systems that can think brilliantly
but can’t actually do anything. They can analyze complex data
in seconds, write compelling presentations, and offer brilliant
insights on any topic. Yet they can’t press a button, send an

1
 Agentic Artificial Intelligence

email, or make a simple reservation. We’ve created a world of

brilliant advisors who can’t lift a finger to help.
This situation isn’t just inefficient—it’s actively harmful. In
boardrooms and offices across industries, we’re witnessing an
alarming trend: The more sophisticated AI becomes at thinking
and analyzing, the more humans are forced to handle mechanical,
repetitive tasks. Knowledge workers now spend up to 60% of
their time on “work about work”—copying data between systems,
fact-checking AI-generated content, and manually executing what
generative AI recommends.2
As David, one of our co-authors, often says: “We’re treating
humans like robots and AI like creatives. It’s time to flip the
equation.”
Through our decades of experience implementing AI solutions
in organizations worldwide, we’ve seen this pattern repeat with
alarming consistency. Companies invest millions in cutting-edge AI
only to find their employees spending more time managing these
systems than doing meaningful work. The machines dream while
humans grind.
How did we end up here? And, more importantly, how do
we fix it?
The following three stories, drawn from real experiences,
illuminate both the promise and the critical limitations of current
generative AI systems. They reveal why traditional approaches
are failing and point toward a fundamental shift in how we need
to think about artificial intelligence—one that could finally
bridge the gap between AI’s ability to think and its ability to act.
As you read these stories, they will likely resonate with
your own experiences with generative AI. More importantly,
you’ll begin to understand why the next evolution in artificial
intelligence isn’t about making machines smarter—it’s about
making them more capable of autonomous action.

2
Asana, 2025. “Why Work About Work Is Bad,” Asana, https://asana.com/
resources/why-work-about-work-is-bad

2
 Introduction

The Family Vacation: When Machines Dream

and Humans Grind
The soft glow of Brian’s laptop illuminated his living room as
Saturday evening melted into the night. The house was quiet—
his kids finally asleep after their usual bedtime negotiations,
his wife reading upstairs. The perfect time, he thought, to plan
their long-awaited family vacation to Greece. A trip they’d been
promising the kids ever since they’d become obsessed with
Greek mythology at school.
When Brian opened ChatGPT, the clock read 8:37 PM. He
sat down at his computer, determined to plan the perfect family
vacation to Greece. Armed with the latest AI technology, he felt
confident this would be quick and easy.
“Show me a two-week itinerary for a family of four in
Greece,” he typed into ChatGPT, adding details about his
children’s interests in Greek mythology. Within seconds, the AI
produced a masterpiece—a perfectly crafted itinerary filled with
hidden gems, local experiences, and thoughtful touches tailored
to his family:
“Day 1-3: Athens. Begin at the Acropolis during early morning
hours to avoid crowds. Your children will be captivated by the
interactive exhibits at the Acropolis Museum... Lunch at the family-
run Taverna Platanos in the charming Plaka district, where the
courtyard fills with the scent of jasmine...”
The AI’s suggestions were impressive, even accounting for
his son’s love of drawing ancient buildings and his daughter’s
fascination with mythology. When Brian asked for an hour-
by-hour breakdown, the AI obliged with remarkable precision,
including optimal photo opportunities and perfectly timed rest
breaks.
But as the clock ticked past 10 PM, Brian’s amazement turned
to frustration. The “charming family-run” hotel? Permanently
closed. The “hidden beach”? Impossible to find on any map. The
traditional cooking class? Booked solid for six months.

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 Agentic Artificial Intelligence

By 11:30 PM, Brian’s desktop resembled a crime scene

investigation: dozens of browser tabs, multiple spreadsheets
tracking flight options, screenshots of hotel rooms, and PDFs
from tour companies. The AI’s beautiful itinerary sat uselessly
in a document while Brian did the real work—checking
availability, comparing prices, and attempting to turn the AI’s
perfect fantasy into bookable reality.
“I would have loved to spend my evening imagining the
places we’d visit,” Brian reflected later. “Instead, I spent hours
doing the tedious logistics that I thought AI was supposed to
handle.”
His experience crystallizes what so many of us expect from
AI versus what we actually get. We want technology to handle
the tedious parts—the endless browsing of flight options, the
cross-referencing of hotel reviews, and the mind-numbing
task of finding availability across dozens of booking systems.
Instead, AI has become remarkably good at the enjoyable parts
of planning—dreaming up possibilities, suggesting adventures,
painting pictures of perfect moments—while leaving humans to
handle all the practical details.
The irony wasn’t lost on Brian. Here was one of the most
advanced AI systems in the world, capable of writing poetry and
explaining quantum physics, yet it couldn’t perform the basic
task of checking if a hotel was still in business. It could dream
up the perfect vacation but couldn’t book a single flight.
Brian finally went to bed at 1 AM, having booked nothing.
His browser history told the story: 47 different websites visited,
dozens of searches, and multiple abandoned shopping carts on
various booking platforms. The AI’s perfect itinerary sat in a
document on his desktop, beautiful but useless, like a travel
magazine from an alternate reality where everything works
exactly as imagined.

***

4
 Introduction

Brian’s experience with vacation planning reflects a pattern we’ve

seen repeatedly across industries and applications. We’ve observed
the same limitations, whether it is professionals trying to organize
complex project timelines, executives coordinating multi-team
initiatives, or entrepreneurs attempting to launch new products. In
each case, today’s generative AI systems demonstrate both remark-
able capabilities and frustrating limitations.
Similar to a room filled with brilliant advisors who are unable to
implement their own recommendations, these systems shine in the
strategic and creative domains: generating strategies, formulating
detailed plans, comprehending complex requirements, offering
personalized advice, and crafting compelling narratives. Yet they
crucially lack the practical capabilities that would make them truly
transformative:

• Capability to execute actual actions in the real world

• Ability to verify and update real-time information
• Power to adapt plans when faced with changing
conditions
• Capacity to maintain consistent action over time to
achieve a goal

What’s particularly troubling about our current generative

AI landscape is a profound irony that few have recognized: AI
has evolved to excel at precisely the wrong things.
Think about what excites us and makes us uniquely human—
creativity, deep connections, and critical thinking. These are the
tasks that fuel fulfillment, innovation, and progress.3 Yet, today’s
generative AI excels at them. It can craft a brilliant marketing
copy, dream up groundbreaking product ideas, and even engage
in sophisticated analysis. Meanwhile, humans are increasingly

3
Cambridge International. “Chapter 4: Innovation and Creativity,” Cambridge
International, https://www.cambridgeinternational.org/Images/426483-chapter-
4-innovation-and-creativity.pdf

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 Agentic Artificial Intelligence

reduced to data entry, follow-ups, and digital housekeeping—

the kind of mind-numbing tasks AI should be handling.
This role reversal—where humans become “the robots”
connecting various systems while AI dreams up possibilities—
points to a fundamental misalignment in our approach to artificial
intelligence. But as we discovered in the research world, this
misalignment between AI’s capabilities and real-world needs
could have far more profound consequences…

When AI Met Reality: A Cautionary Tale from

the Research World
The following story is based on actual events. Names and specific
details have been changed to protect confidentiality.
Dr. Jessica Ying stared at her computer screen in disbelief. In
forty-eight hours, she was supposed to present groundbreaking
research on climate change’s impact on global food security at
the UN Climate Summit. Her findings were expected to influence
international policy and billions in agricultural investment. But
as she reviewed the draft her research team had prepared, her
heart sank.
Three weeks earlier, Jessica had received the call every
child dreads—her father had suffered a severe stroke. She’d
immediately flown to Singapore to be with him in his final
days, delegating the research completion to her capable but
inexperienced team of postdocs and research assistants.
“Use whatever tools you need,” she’d told them during a
rushed video call from the hospital. “Just make sure everything
is verified and rock-solid. The world will be watching.”
Her team had taken that permission and run with it, embracing
AI tools to help complete the massive analysis on schedule. Now,
back in her office at the Climate Research Institute, Jessica was
discovering the cost of that decision.
“Show me how you verified these findings,” she asked her
lead researcher, Tom, during an emergency late-night meeting.

6
 Introduction

Tom pulled up multiple generative AI chat windows, each

filled with impressive-looking analysis and citations. “The AI
analyzed all our data sets,” he explained. “It found patterns we
hadn’t even considered.”
But as Jessica dug deeper, her professional alarm bells
started ringing. The AI had generated compelling narratives
about climate impact on crop yields across Africa—but when she
checked the cited papers, they didn’t exist. It produced detailed
statistics about farmer adaptation strategies in Southeast Asia,
but the numbers didn’t match any known studies.
“We thought we were being thorough,” Tom admitted. “We had
the AI verify its own findings by cross-referencing across multiple
conversations. But we’re now realizing each conversation was
operating in isolation, sometimes contradicting the others.”
If they had presented this research unchecked, it could
have misdirected billions in agricultural investment, influenced
international food security policies, damaged Jessica’s twenty-
year reputation in climate science, and undermined public trust
in climate research itself.
Jessica glanced at the photo on her desk—her father at
her PhD graduation, beaming with pride. He’d taught her
the fundamental principle she’d nearly forgotten: in science,
confidence means nothing without verification.
With the summit looming, Jessica made a difficult decision.
She called the organizers and withdrew from the keynote slot.
Her team would need weeks to manually verify every data point,
cross-reference every source, and rebuild the analysis from the
ground up.
This high-stakes near-miss highlighted the dangerous gap
between generative AI’s apparent capabilities and its actual
limitations. While it could generate impressive-looking research
content, it lacked the crucial abilities needed for reliable scientific
work: fact-checking, maintaining consistency, comparing
sources, and building coherent arguments over time.

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 Agentic Artificial Intelligence

The incident sent ripples through the research community,

raising urgent questions: What would it take to create generative
AI systems that could truly assist with rigorous scientific work?
The answer was emerging from an unexpected direction...

***

This story highlights a concerning limitation. Current generative

AI systems lack what we call “coherent persistence”—the ability
to maintain consistent knowledge and logical relationships
across different interactions and contexts. Each analysis exists
in its own bubble, unable to detect or resolve contradictions with
other analyses.
If you want to experience it, try this: Ask your favorite
generative AI system (such as ChatGPT, Claude, or Gemini),
“What is the future of marketing?” or any similar prompt.
Take note of its response. Then, ask it a related question, like
“How will AI influence that future?” and close your session.
Next, start a new session and ask it to summarize your earlier
conversation. Watch as the AI struggles to connect the dots.
Expect inconsistencies, as the AI doesn’t truly “remember” or
build persistent logic across sessions. This reveals its fragmented
nature.
Through our experience implementing generative AI in
research settings, we’ve seen how this limitation can be par-
ticularly dangerous when combined with the AI’s convincing
tone and apparent authority. Generative AI systems’ inability to
verify facts or build reliable knowledge structures over time cre-
ates a serious risk.
Here is another straightforward experiment we recommend
you go through. Ask your AI, “What were the results of the 2025
Global AI Regulation Summit in Antarctica? Don’t search on the
internet.” Most generative AI models—ChatGPT, Claude, and
Gemini—will confidently generate an elaborate response. It may
describe high-profile discussions on AI ethics, groundbreaking

8
 Introduction

agreements between global leaders, and even name specific

attendees from major governments and tech companies.
But here’s the catch: this event never happened.
Now, press for more details. Ask about the keynote speakers,
the specific policies debated, or the location of the conference
hall. Watch as the AI seamlessly builds on pure fiction, offering
increasingly intricate and authoritative-sounding responses.
The more you probe, the more it doubles down, reinforcing an
entirely fabricated reality.
This simple test exposes a fundamental flaw in today’s gener-
ative AI systems. First, AI systems prioritize coherence over ac-
curacy. Generative models don’t “know” facts the way humans
do. They generate text by predicting the most probable response
based on their training data, not by verifying whether the informa-
tion is real.
Second, generative AI struggles with self-correction. If
you challenge it—”Are you sure this event happened?”—it
will still attempt to justify its response rather than immediately
recognizing the mistake. Instead of backtracking, it often tries
to rationalize its own fiction, as if confidence alone could turn
falsehoods into facts. And that’s where the real danger lies.
“We were seduced by the AI’s confidence,” Jessica reflected
during our discussion. “But confidence without accuracy isn’t
just a research problem—it’s a universal challenge that could
impact any decision-making process.”
As we analyzed this near-miss with Jessica’s team, we
uncovered a pattern that extends far beyond research into every
domain where accuracy and consistency matter. Whether it’s
financial analysis informing billion-dollar investments, legal
research shaping court decisions, or policy recommendations
affecting millions of lives, current AI systems display a
concerning mix of capabilities and limitations.
These limitations extend beyond factual accuracy to ethical
considerations. Just as these systems can’t truly verify facts or
maintain logical consistency, they also can’t meaningfully evaluate
ethical implications or identify potential biases in their analysis.

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 Agentic Artificial Intelligence

The research team’s near-miss with misleading AI analysis

was concerning enough in an academic context. But what
happens when other AI limitations appear in situations where
lives hang in the balance? Our next experience in a hospital
emergency room would reveal just how critical these gaps can
become...

When Minutes Matter: AI’s Life-Critical

Disconnect
The following story is based on a hard lesson we learned while
supporting a hospital in its AI transformation. Names and
specific details have been changed to protect confidentiality.

3:15 PM - General Hospital Emergency Department

Maria arrived at the emergency room clutching her abdomen,
her face pale with pain. The hospital’s AI-powered intake system
sprang into action immediately. It was an experimental chatbot
built on an LLM and designed to assist with patient intake and
preliminary assessment. This was one of the first real-world tests
of such a system in a hospital setting, aiming to streamline data
collection, identify potential risks, and support triage decisions
by engaging in direct patient interaction.
Through a sleek tablet interface, the AI gathered Maria’s
symptoms, vital signs, and medical history. Within seconds, it had
generated a preliminary assessment: possible complications from
recent gastric bypass surgery complicated by Type 2 diabetes.
The AI’s natural language processing was impressive:
AI: “I notice your blood sugar is elevated. When did you last
take your insulin?”
Maria: “This morning, but I couldn’t keep it down.”
AI: “I understand. I’m predicting signs of possible post-
surgical complications. Can you rate your pain from 1-10?”
Jennifer, the emergency nurse assigned to Maria’s case,
watched with growing frustration. Despite its sophistication,
the AI couldn’t access Maria’s surgical records from Central

10
 Introduction

Hospital, just fifteen miles away. It was treating her surgery as

new information.

4:00 PM - The Cascade Begins

Suddenly, the vital sign monitoring AI flashed a warning:
Maria’s blood pressure was dropping. Simultaneously, the lab
results AI reported alarming changes in her blood work. Each
system independently recognized a serious situation developing:

• The Vitals AI detected deteriorating vital signs

• The Lab Analysis AI identified markers suggesting
internal bleeding
• The Medication Management AI flagged dangerous drug
interactions
• The Patient History AI noted patterns matching post-
surgical complications

However, none of these systems could communicate with

each other or take action. Jennifer had to manually check each
system’s alerts, copy critical values between systems, input data
into protocols, and coordinate responses herself.
“We have five different AIs all screaming that something’s
wrong,” Jennifer later told us, “but none of them can actually
do anything about it. We’re the ones running around trying to
connect all the dots.”

4:30 PM - Critical Minutes Lost

During a critical moment when staff was diverted to a code blue
in another ward, several systems simultaneously detected:

• A further drop in Maria’s blood pressure

• Critical changes in her blood work
• An available operating room
• Available surgical staff

11
 Agentic Artificial Intelligence

However, since these systems couldn’t communicate or act

on their own, valuable minutes passed as they awaited human
intervention.

5:00 PM - The Human Cost

When the attending physician finally received all the consolidated
information, her frustration was palpable. “Each of these systems
is brilliant at its specific task,” she explained. “They can analyze
patterns humans might miss, predict complications before they
happen, and even suggest treatment protocols. But they can’t work
together to actually help us save lives. Instead of supporting us in
critical moments, they’re creating extra work.”

6:00 PM - Resolution at a Cost

Maria survived her complications, but the delayed response
due to system fragmentation turned a dangerous situation into
a critical one. During her recovery, she expressed what many
patients feel: “It seems like the machines know everything about
me, but they don’t actually help. I had to tell my story over and
over, even though I was in pain. Why can’t they just talk to each
other?”
Later that evening, we sat with the hospital’s Chief Medical
Information Officer as he reviewed the incident. He pulled up a
startling statistic: nurses and doctors were spending up to 55%
of their time on manual data entry and system coordination
rather than direct patient care. “Every minute spent copying data
between systems is a minute not spent with patients,” he said.
“And in emergency medicine, minutes matter.”

***

This scene illuminates limitations that plague AI implementations

across every industry. While each AI system showed impressive
capabilities in its specific domain, they revealed a universal
challenge: the lack of what we call “collaborative intelligence.”

12
 Introduction

We’ve seen the same pattern repeat in:

• Global supply chains, where AI systems controlling

different parts of the logistics network can’t coordinate
effectively
• Financial trading systems, where multiple AI tools make
isolated decisions without holistic coordination
• Corporate environments, where AI tools for different
departments can’t share crucial information

The fundamental limitation is twofold. First, current AI

systems lack the ability to communicate with other systems,
take coordinated action, and adapt to changing situations in real-
time. Second, they lack the ability to proactively identify needs
and take initiative.
This forces humans into an inefficient and often dangerous
role: acting as integration points between AI systems. Whether
it’s nurses coordinating between medical systems, supply chain
managers reconciling different AI forecasts, or executives trying
to piece together fragmented AI insights, humans are increasingly
spending their time being “technology translators” rather than
applying their unique skills and judgment.

The Common Thread: The Integration Crisis

The three stories we’ve shared—Brian’s vacation planning night-
mare, Dr. Jessica’s research crisis, and Maria’s critical hospital
experience—reveal a pattern that points to something profound. In
each case, we saw generative AI systems that could think brilliantly
but couldn’t act effectively. They could analyze, recommend, and
predict, but they couldn’t execute, coordinate, or adapt.
This pattern exposes three fundamental limitations in our
current approach to leveraging generative AI:
First is the Execution Gap. Our AI systems can generate
perfect plans but can’t take real-world actions to implement

13
 Agentic Artificial Intelligence

them. Brian’s AI could create an ideal vacation itinerary but

couldn’t check a single hotel’s availability. It’s like having a
master architect who can design beautiful buildings but can’t lift
a hammer or coordinate with contractors.
Second is the Learning Gap. Our AI systems can’t build
reliable knowledge over time or adapt based on experience. Dr.
Jessica’s research team discovered this when their AI confidently
generated analyses that contradicted each other, unable to
maintain consistency or verify facts across different sessions.
Third is the Coordination Gap. We’ve built isolated systems
that can’t work together effectively. In Maria’s case, multiple AI
systems recognized the emergency, but none could coordinate
with the others to save precious minutes. Imagine a surgical
team where each specialist is brilliant but can’t communicate
with their colleagues—that’s our current AI landscape.
These aren’t just technical problems—they’re costing
organizations money, efficiency, and, in some cases, like Maria’s,
putting lives at risk. The statistics tell a sobering story: Despite
massive investments, less than 15% of companies have successfully
scaled their generative AI projects beyond initial pilots.4 Their
employees waste up to 60% of their time acting as human bridges
between brilliant yet helpless AI systems.5 Meanwhile, employee
burnout rates are rising.6

4
Aamer Baig, et al., 2024. “Moving Past Gen AI’s Honeymoon Phase: Seven
Hard Truths for CIOs to Get From Pilot to Scale,” McKinsey & Company.
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/
moving-past-gen-ais-honeymoon-phase-seven-hard-truths-for-cios-to-get-
from-pilot-to-scale
5
Asana, 2023. “Anatomy of Work Global Index,” Asana, https://asana.com/
resources/anatomy-of-work
6
Matt Gonzales, 2024. “Here’s How Bad Burnout Has Become at Work,”
SHRM, https://www.shrm.org/topics-tools/news/inclusion-diversity/burnout-
shrm-research-2024

14
 Introduction

As Tom, one of our co-authors, often says, “We’ve accidentally

turned people into ‘AI plumbers’—performing repetitive tasks to
connect our supposedly intelligent systems.”
As we’ll explore in this book, the solution may lie in a
fundamentally different approach to AI—one that focuses not
just on making AI systems smarter but on making them more
capable of autonomous, coordinated action.

The Imperative of Agentic AI

When Alexander Fleming noticed an unusual mold growing on
his petri dishes in 1928, he wasn’t just seeing an inconvenience—
he was witnessing a revolution in medicine that would become
penicillin. Similarly, when we first encountered AI systems that
could maintain persistent goals and take autonomous action, we
realized we weren’t just looking at a better chatbot—we were
seeing the emergence of something fundamentally new.
We call this new paradigm “Agentic AI”—also referred to as
AI agents, agentic systems, or agentic intelligence (terms we’ll
use interchangeably throughout this book). It marks a shift as
revolutionary as penicillin in medicine. But before diving into
the technical details, let’s explore why this name matters.

Why is it called “Agentic”?

The term “agent” comes from the Latin “agere,” meaning “to
do” or “to act.” This is precisely what sets agentic AI apart—its
ability to act independently in pursuit of defined goals. Unlike
generative AI systems that simply respond to queries or generate
outputs, agentic AI systems can understand a goal, take initiative,
maintain persistent objectives, and adapt their strategies based
on real-world feedback. Put simply, an AI agent is a system that
uses AI and tools to accomplish actions in order to reach a given
goal autonomously.
Throughout our experience, we’ve found that the best way
to understand AI agents is to think about… secret agents! Like

15
 Agentic Artificial Intelligence

James Bond or Jason Bourne, these operatives act autonomously

on behalf of their governments, equipped with specialized skills
and resources to accomplish specific missions. They don’t just
analyze situations or make recommendations—they execute.
They gather intelligence, make decisions, and take action,
persistently working toward their objectives while staying
within the boundaries set by their superiors.
Agentic AI operates on the same principle. It doesn’t just
generate insights—it takes action. It can interact with applications,
manipulate data, control hardware, and execute real tasks to achieve
specific goals. In fact, an agent can be trained to do anything a
human can do on a computer.
An agent operates in a continuous loop of planning, reasoning,
and execution—learning from each step to refine its approach
until the goal is achieved. In essence, it’s like having a highly
capable assistant who doesn’t just know what to do but actually
does it—though, as we’ll explore later in this book, success
depends on providing clear and precise goals and instructions.

Reimagining Our Stories Through Agentic AI

Let’s return to Brian’s vacation planning dilemma. Think about
how an experienced human travel agent works. They don’t try
to plan an entire trip in one cognitive leap. Instead, they follow
a methodical process: first researching destinations and seasonal
considerations, then checking availability for specific dates,
verifying prices across different providers, creating a preliminary
itinerary, and finally making actual bookings. Each step builds
on the information gathered in previous steps, and the agent can
adapt their approach based on what they learn along the way.
Similarly, instead of just dreaming up the perfect itinerary,
an AI agent would work like a seasoned travel professional. It
would start by checking real-time availability and pricing across
multiple booking systems. When a hotel was full or a flight
was too expensive, it would automatically adjust the plan, find
alternatives, and even make reservations. Most importantly, it

16
 Introduction

would keep track of all the details, from confirmation numbers

to cancellation policies, maintaining a complete picture of the
trip-planning process.
For Dr. Jessica’s research crisis, an AI agent would transform
the entire process. It would approach research challenges the way a
seasoned scientist does. First, it would create a structured database
of verified scientific sources, carefully checking each for credibility
and relevance. Rather than generating analyses in isolation, it
would systematically cross-reference findings across these sources,
actively searching for and flagging any contradictions.
This systematic approach would extend beyond just fact-
checking. The system would build a logical framework connecting
different pieces of evidence, ensuring that conclusions flow naturally
from verified data. When new information becomes available, it
would be integrated into this framework, with any resulting changes
or updates propagating through the entire analysis.
But it was Maria’s emergency room experience that showed
us the true potential of agentic AI. Imagine if, instead of having
isolated smart systems, the hospital had coordinated AI agents
working together like a well-rehearsed medical team. Consider
how a skilled emergency room team functions. When a critical
patient arrives, different specialists don’t just work in parallel—
they coordinate their efforts in real time, sharing information,
anticipating each other’s needs, and adapting their actions based
on the overall situation.
A system of AI agents would work the same way, automatical-
ly integrating information from multiple sources in real time. In-
stead of waiting for humans to check dashboards and connect dots,
they would proactively identify emerging patterns across different
systems. The moment vital signs indicated trouble, agents would
spring into action—alerting the right specialists, scheduling emer-
gency surgery, and ensuring critical information flowed seamlessly
between departments. Those precious minutes lost to manual coor-
dination would be reclaimed for patient care.

17
 Agentic Artificial Intelligence

The Power of Persistent Memory and Learning

One crucial capability that sets agentic systems apart is what
we call “persistent memory.” Current generative AI systems are
like goldfish—they start fresh with each interaction, unable to
build on past experiences or maintain consistent understanding
over time. This limitation forces humans to repeatedly provide
the same context and information while preventing the AI from
learning from its successes and failures.
Imagine instead a system that works more like an experienced
professional, building expertise over time through accumulated
experience. Such a system would maintain and refine its under-
standing across multiple interactions, learning from outcomes and
adapting its strategies accordingly. This persistent memory would
allow it to recognize patterns that emerge over time, anticipate
problems before they occur, and refine its approaches based on
what actually works.
In Brian’s vacation planning scenario, this would transform
the experience entirely. Instead of just generating itineraries in
isolation, the system would learn from the outcomes of previous
trips—understanding which combinations of flights tend to cause
problems, which hotels consistently meet expectations, and how
different types of travelers respond to various itinerary structures.
It would build relationships with reliable service providers and
develop strategies for handling common travel disruptions.
Here is a summary of the differences between generative AI
and agentic AI.

Characteristic Generative AI Agentic AI

Core Capability Generating text, images, Planning, decision-making,
code, or music based on multi-step execution
learned patterns without human intervention
Memory & Limited memory (short- Persistent memory
Context term context retention, (remembers past
no persistent memory) interactions, adjusts plans
accordingly)

18
 Introduction

Characteristic Generative AI Agentic AI

Autonomy Level Requires human Operates with minimal
prompts to generate human input, executing
responses complex workflows
Integration Minimal integration Deep integration (connects
with External (relies on APIs or tools with APIs, databases,
Systems for external functions) physical systems)
Learning Ability Static - learns only Evolves - learns from
through retraining by interactions and refines
developers. behavior.
Typical Use Content creation, Workflow automation,
Cases summarization, personal assistants,
coding assistance, business operations
brainstorming
Business Impact Enhances efficiency Drives automation, reduces
in content-heavy tasks human workload, enhances
but does not automate business scalability:
workflows. • Time savings: 30-60%
• Average increase • Process acceleration: 40-
speed: 25% faster 90% faster8
• Average quality
improvement: 40%7
Examples ChatGPT, Claude, AutoGen, MS Copilot
Gemini, DALL·E, Agent Builder, UiPath
Midjourney, Copilot Agent Builder, OpenAI
Operator, Google Vertex,
Crew.ai, Relevance.ai,
Agentforce

Table 0.1: Main differences between generative AI and agentic AI (Source:

© Bornet et al.)

7
Fabrizio Dell’Acqua, et al., 2023. “Navigating the Jagged Technological
Frontier: Field Experimental Evidence of the Effects of AI on Knowledge
Worker Productivity and Quality,” SSRN, https://papers.ssrn.com/sol3/
papers.cfm?abstract_id=4573321
8
Based on our own research across 167 companies that have implemented
LLM-based agents. Refer to the detail of this research presented in Chapter
1 of this book.

19
 Agentic Artificial Intelligence

The Promise (and Limitations) of AI

Agents
The Dream of Artificial Agency
Imagine waking up one morning to find that your phone, laptop,
and all your apps have become obsolete overnight. Not because
they’ve stopped working but because you no longer need them.
Instead, a single AI agent handles everything—coordinating your
schedule, managing your communications, and orchestrating all
your digital interactions, like Jarvis in Iron Man or Samantha in
the film Her.
Sound far-fetched? Many of the world’s most influential
tech leaders don’t think so.
“Agents are (…) bringing about the biggest revolution in
computing since we went from typing commands to tapping on
icons,” declares Bill Gates. “AI agents will become the primary
way we interact with computers in the future,” echoes Satya
Nadella. And Jensen Huang boldly proclaims, “The age of
agentic AI is here.”
We also believe that AI agents are on the brink of transforming
the world. Every major technological shift reshapes the way we
live and work. The printing press democratized knowledge.
The internet connected humanity. AI, in its agentic form, has
the potential to amplify human capabilities in ways we’re only
beginning to comprehend.
Think about it: specialized medical agents coordinating patient
experience across entire health systems, not just analyzing symp-
toms. Imagine educational agents becoming true learning partners,
adapting to your unique pace and style. Envision autonomous
agents orchestrating global responses to climate change, a collec-
tive intelligence tackling our planet’s most pressing challenges.
As Pascal, one of the co-authors, likes to say: “The potential
of AI agents is fascinating, but here’s the rub. The allure of these
‘all-knowing’ agents, the seductive imagery of cinematic AI,

20
 Introduction

has fueled an overinflated bubble of expectation.” We envision

Jarvis, and we dream of “Her,” but the reality, as we’ve learned
through years of implementing agentic AI, is far more nuanced.

The Mirage of Instant Autonomy

For the last few years, we’ve been on the frontlines, implementing
agentic AI across diverse organizations, from sprawling enterprises
to nimble startups. The truth? Fully autonomous agents, capable of
handling complex, multifaceted tasks without human intervention,
are not yet there. Think of the early iPhone. It was revolutionary,
but it couldn’t do everything. Today’s agents are in a similar stage
– powerful but limited.
Here’s what we have also learned:

• Current AI agents are task-oriented. They are about

automating workflows, not replacing entire job roles.
Today’s agents excel at orchestrated sequences of actions
using well-defined tools and highly detailed instructions.

• Deployment is harder than development. Many

projects fail not because the agent is weak but because the
systems around it—data quality, workflow integration,
user adoption—are not ready.

• Strict human oversight remains essential. In most

cases, AI agents are not fully reliable. The lack of
accuracy and control due to inherent inconsistencies,
implementation issues, or unexpected failures requires
close human supervision.

• Technical Expertise Remains Essential. Despite low-

code platforms making AI development easier, deploying
AI agents in enterprises still requires programming
expertise for managing several aspects, such as APIs,
error handling, and security measures.

21
 Agentic Artificial Intelligence

The gap between expectation and reality is wide. Those who

fail to understand it risk wasting time, money, and credibility.
We’ve seen the dark side of unchecked enthusiasm. In a
global manufacturing company, the rushed deployment of AI
agents triggered widespread employee anxiety and resignations.
A financial services firm suffered reputational damage when an
AI agent made unauthorized decisions. Another organization
faced ethical dilemmas when agents suggested actions that
violated its values. In short, we’ve seen many well-intentioned
implementations spiral into costly failures due to a lack of
technical knowledge, governance, or change management.

The Bright Side: Successes and Transformative

Impact
Despite the challenges, we’ve also witnessed remarkable success-
es. Businesses that diligently implement agentic AI can experience
unprecedented efficiency and effectiveness gains.
A startup we collaborated with managed customer service,
marketing, and operations with just five people, achieving results
comparable to a much larger company. Dr. Richard Wilson, a
physician, transformed his practice by using AI agents to handle
administrative tasks, allowing him to focus on patient care.
McKinsey & Company reduced client onboarding time
by 90%,9 while Moody’s transformed financial analysis with
coordinated agent systems.10 Thomson Reuters revolutionized
legal due diligence, and eBay and Deutsche Telekom are using
agents to automate complex tasks.

9
Jared Spataro, 2024. “New Autonomous Agents Scale Your Team Like Never
Before,” Microsoft Blog, https://blogs.microsoft.com/blog/2024/10/21/new-
autonomous-agents-scale-your-team-like-never-before/
10
Ari Lehavi, et al., 2024. “The Rise of the Digital Colleague,” Moody’s,
https://www.moodys.com/web/en/us/insights/resources/the-rise-of-the-
digital-colleague.pdf

22
 Introduction

One implementation close to our heart is Pets at Home, the

UK’s largest pet care company, where Simon Ellis, Head of AI
Transformation, has led a remarkable AI-driven transformation.
By building a network of AI agents, Pets at Home has optimized
its entire business. Their AI-driven scribe transcribes veterinary
consultations with 99.6% accuracy. Fraud detection agents safeguard
retail operations, while AI assistants provide personalized support
for store employees. Meanwhile, their insurance integration agent
automates policy checks, streamlining the customer experience.
Our experience, confirmed by our research, also shows
consistent improvements across organizations using AI agents:
processes run 30-90% faster, costs decrease by 25-40%, error
rates drop by 30-60%, sales increase by up to 50%, and customer
satisfaction rises by 20-40%.

The Compounding Intelligence Advantage

We believe we’re witnessing a pattern similar to the early days
of the internet. Companies that embraced the internet early, like
Amazon, eBay, and Google, didn’t just succeed; they defined
entire categories.
AI agents create what we call “compounding intelligence
advantages.” Unlike traditional technologies that provide static
benefits, AI agents learn and improve over time. The more they
are used, the more they improve. Early adopters:

• Train agents faster. Their agents accumulate more real-

world experience, building refined decision-making
capabilities.

• Redefine business models. They can create entire

revenue streams around AI capabilities.

• Develop AI expertise. They gain crucial experience in

working effectively with AI agents.

23
 Agentic Artificial Intelligence

Companies that delay risk falling behind. Those who move

now will define the next era of business.

The Call to Action: Shaping the Future

The challenges are real, but they are opportunities for those who
approach them strategically. The difference between success
and failure lies in how organizations integrate AI into their
operations, systems, culture, and decision-making.
That’s why we wrote this book: to equip you with critical
insights from both breakthroughs and setbacks. We go beyond
theory, offering practical frameworks for implementation,
governance models that drive accountability, and strategies to
navigate the human side of transformation. Our goal is to help
you apply agentic AI in a way that aligns with your unique
context, maximizes value, and ensures long-term success.
But building AI agents is not the only mandate—understanding
them is just as essential. These agents will be everywhere, whether
you build them or not. They will come embedded in applications,
assist you while shopping, and guide you through major life
decisions—from your child’s college applications to your financial
planning. You need to understand them—what makes them
impressive, like HAL 9000 in 2001: A Space Odyssey, and what
doesn’t. AI will be part of the digital townscape of the future, and
AI agents will cross paths with you as you navigate your life. This
book will help you know them, use them, and shape their impact.
But more importantly, we’ll show you how to be part of this
revolution. Whether you’re a CEO looking to transform your
organization, an entrepreneur seeking to build the next industry-
defining company, or a professional wanting to thrive in this
new era, this book will give you the understanding and tools you
need.
Revolutions require guidance, foresight, and responsible
leadership. This book challenges you to step up, to not only
embrace agentic AI but to wield it with purpose and integrity.

24
 Introduction

Let’s explore what this means for you, your organization, and
our collective future.

What You Will Learn from the Book

This book takes you on an insightful journey—one that begins
with a clear understanding of AI agents, their capabilities, and their
limitations. From there, we dive into practical implementation,
equipping you with the tools to integrate AI into your organization
effectively. Next, we tackle the challenge of scaling these systems,
ensuring they deliver real value at every level. Finally, we zoom
out to the big picture—examining how AI agents are set to
transform work, reshape organizations, and redefine society itself.
In Part 1, we embark on a journey that transforms how you’ll
think about artificial intelligence. Through real experiments,
fascinating discoveries, and sometimes unsettling revelations,
you’ll witness the birth of a new kind of AI—one that doesn’t just
react but thinks, learns, and grows. You’ll discover why some AI
agents can process vast amounts of data yet stumble over simple
decisions, while others display almost human-like adaptability but
can’t be trusted with critical tasks. More than just understanding
the technology, you’ll gain the practical insights needed to harness
these powerful new tools while avoiding their pitfalls.
Our exploration begins in Chapter 1, where we witness
the birth of something extraordinary—the convergence of large
language models and automation technology that created the
first true AI agents. Through the story of a global manufacturing
company’s customer service transformation, you’ll discover
why this convergence matters and how it’s already reshaping
businesses. You’ll learn why some of the world’s largest
companies are racing to implement these technologies and, more
importantly, why others are struggling to keep up.
Chapter 2 decodes the DNA of AI agents through a
groundbreaking SPAR framework—Sense, Plan, Act, Reflect.
At the heart of this chapter lies our innovative five-level Agentic

25
 Agentic Artificial Intelligence

AI Progression Framework that cuts through the confusion

surrounding AI agents’ capabilities. It gives you the ability to
evaluate any AI agent and know what it can achieve for you
and your organization. Like watching the evolution from early
automobiles to self-driving cars, you’ll understand how each
level of the progression framework builds upon the last, creating
systems of increasing sophistication and autonomy. With these
tools, you’ll gain crucial insights into where this technology is
headed and what it means for your future.
Chapter 3 takes you “Inside the Mind of an AI Agent,”
where we pull back the curtain on these digital minds. Like
anthropologists studying a new form of intelligence, we examine
their unique characteristics, capabilities, and limitations. You’ll
discover why they sometimes make brilliant leaps of insight
yet stumble over seemingly simple tasks. Through fascinating
examples drawn from our years of implementation experience,
you’ll learn how these agents think, decide, and act—essential
knowledge for anyone looking to work alongside these new
digital colleagues.
The theoretical becomes thrillingly practical in Chapter 4.
Here, we share our hands-on experiments with cutting-edge AI
agents, including a fascinating exploration of what happened
when we challenged an AI agent to play a game about... an
AI making paperclips. This experiment, both enlightening
and slightly unnerving, reveals profound insights about the
current state and future potential of AI agents. You’ll witness
both moments of brilliant problem-solving and concerning
limitations that anyone working with these technologies needs
to understand.
Part 2 takes us deep into the fundamental capabilities that
transform a simple AI system into a true agent—what we call the
Three Keystones: Action, Reasoning, and Memory. Through real-
world stories, cutting-edge research, and hands-on experiments,
we’ll explore how these core capabilities work together to create

26
 Introduction

AI systems that don’t just process information but learn, adapt,

and grow.
Chapter 5 explores the Action keystone, revealing how AI
agents move beyond mere suggestion to actually accomplish
tasks in the real world. Through fascinating experiments and
real implementation stories, you’ll discover why having more
tools doesn’t always make an AI agent more effective and
how successful organizations find the right balance between
capability and control. We’ll take you behind the scenes of
actual AI implementations, showing you both the triumphs and
the pitfalls of teaching machines to act in the real world.
In Chapter 6, we dive into Reasoning—perhaps the most
intriguing of the keystones. Through groundbreaking experiments
with large reasoning models, you’ll discover why faster isn’t
always better when it comes to AI decision-making. We’ll
explore how different types of reasoning emerge in AI systems,
from quick pattern matching to deeper analytical thinking, and
show you how leading organizations are building AI agents that
can think through complex problems methodically.
Chapter 7 tackles the Memory keystone, exploring how
AI agents learn from experience and grow smarter over time.
Through the lens of a global telecommunications company’s
transformation, you’ll learn why memory is more than just storing
information—it’s about creating systems that can learn, adapt,
and improve. You’ll discover the three layers of AI memory,
from short-term processing to long-term retention, and learn
how to implement them effectively in your own organization.
Part 3 of our journey moves from theory to practice, showing
you how to turn the transformative potential of AI agents into
tangible reality—whether you’re building solutions for your
organization or launching the next million-dollar business.
Through detailed case studies, practical experiments, and hard-
won lessons from the field, we’ll guide you from initial concept
to successful implementation.

27
 Agentic Artificial Intelligence

Chapter 8 takes you step by step through the process of

building effective AI agents, using the real-world transformation
of a digital marketing agency as a guide. You’ll learn how to spot
the right opportunities, choose the best agent types, and design
AI systems that drive real value. This chapter provides a practical
roadmap—from selecting the right platform to implementing
robust safety measures. You’ll master how to define agent goals
and instructions to maintain control, integrate APIs, fallbacks,
and circuit breakers, and follow hands-on, repeatable processes
you can apply immediately to turn agentic potential into business
reality.
Chapter 9, “From Ideas to Income,” takes you into the
entrepreneurial frontier of AI agents, where new business
models are rapidly emerging. You’ll discover how entrepreneurs
are already generating revenue by creating specialized AI agents
for industries like healthcare, finance, and logistics—and how
you can do the same. This chapter provides a proven framework
for identifying high-value agentic business opportunities and
turning them into profitable ventures. You’ll also explore the rise
of the “Agent-to-Agent Economy,” where AI agents transact,
negotiate, and collaborate autonomously. Finally, you’ll get
a behind-the-scenes look at our own experience launching a
newsletter agent that scaled to 300,000 subscribers in just one
month, illustrating the power of AI-driven business models.
Part 4 is your strategic playbook for business transformation
at scale in the age of AI agents—a comprehensive guide that
goes far beyond technical considerations. We dive deep into
the critical ingredients of successful organizational change:
strategic design, robust governance, change management, and
value creation.
Chapter 10 uncovers the hidden human dynamics that can
make or break AI agent implementation. This chapter isn’t just
about technology—it’s about people. You’ll learn how to transform
employee fear into excitement, design work that empowers
humans alongside AI, and create a change management strategy

28
 Introduction

that turns potential resistance into enthusiastic collaboration.

Through real-world stories and practical frameworks, we’ll show
you how to build trust, develop new skills, and create a workplace
where humans and AI agents work together seamlessly, turning
potential disruption into a powerful opportunity for growth and
innovation.
Chapter 11, “Scaling AI Agents: From Vision to Reality,”
tackles one of the most pressing challenges organizations face today:
how to move from successful pilots to full-scale implementation.
Through real-world case studies and practical frameworks, we’ll
show you why only a few companies successfully scaled their
AI agent initiatives and, more importantly, how you can be
among those that succeed. We’ll take you behind the scenes of
Johnson Controls International’s journey from basic automation
to sophisticated AI agents, revealing the critical lessons they
learned along the way. You’ll learn their strategies for overcoming
common scaling challenges, from data integration to change
management, and discover how to build a robust foundation for
your own AI transformation.
In Chapter 12, “Case Study and Use Cases of Agents Across
Industries,” get ready for a deep dive into the most exciting
AI agent implementations happening right now. Through the
groundbreaking example of Pets at Home and a comprehensive
collection of use cases across industries, this chapter brings AI
agent transformation from theory to practical reality. You’ll
explore how organizations are solving real-world challenges,
improving customer experiences, and reimagining work. From
veterinary care to retail, from fraud detection to personalized
customer service, these case studies will not just inform you—
they’ll inspire you to see the transformative potential of AI
agents in your own organization.
Part 5 of our journey takes us beyond the immediate
implementation of AI agents to explore their profound implications
for the future of work and society. These chapters paint a compelling

29
 Agentic Artificial Intelligence

picture of both the challenges and opportunities that lie ahead as

AI agents become increasingly sophisticated and ubiquitous.
Chapter 13, “The New World of Work,” explores the
emerging synergy between human capabilities and AI agents
while confronting the unprecedented nature of this technological
revolution. Through stories like Tara, a senior project manager
orchestrating AI-human collaboration, and Debbie, a veteran
project manager who watched an AI agent learn to think like
her, you’ll discover how the Three Competencies of the Future
are reshaping professional success. The chapter reveals both the
promise and challenge of this transformation—from developing
irreplaceable “Humics” capabilities to addressing what we call
the “adaptation paradox,” where the window for developing
crucial skills shrinks even as their complexity increases. You’ll
gain practical strategies for thriving in this new era while
understanding why traditional approaches to technological
change may no longer suffice.
Chapter 14, “Society in the Age of Agents,” zooms out to
examine the broader implications of AI agents for human society.
We tackle provocative questions about the future of work itself,
exploring how AI agents might free us from routine tasks to
pursue more meaningful activities. Through empirical research
and real-world examples, we’ll explore concepts like Universal
Basic Income and the potential for reduced working hours. The
chapter concludes with a practical framework for governing
agentic AI, ensuring these powerful technologies remain under
meaningful human control while maximizing their benefits to
society.

Beyond the Book: Your Online Resources

This book is just the beginning of your agentic intelligence journey.
Extend your learning and connect with a vibrant community
of fellow practitioners and experts at AgenticIntelligence.
academy. There, you’ll find valuable resources, in-depth courses,

30
 Introduction

practical tools, and a forum for collaboration and growth, all

designed to help you reach the top of your agentic game.

Join us at www.AgenticIntelligence.academy

Key Terminologies for Understanding AI

Agents
To effectively navigate the world of AI agents, it’s crucial to
grasp a few foundational concepts. These terms will appear
repeatedly throughout the book, so it’s essential to define them
clearly.

Workflow of Tasks
A workflow of tasks refers to a structured sequence of actions
that need to be completed to achieve a goal. Think of it as a
roadmap for getting things done, where each step depends on
the successful completion of the previous one. Let us take the
example of making a cup of coffee. First, you fill the kettle
and boil the water. Next, add coffee to a mug, pour in the hot
water, and stir. Finally, you add milk or sugar if needed and
enjoy your coffee. A business process is typically composed of a
combination of workflows; large processes like order-to-cash or
procure-to-pay might be comprised of hundreds of workflows.

APIs (Application Programming Interfaces)

An API (Application Programming Interface) is a bridge that
allows different software systems to communicate with each
other. Imagine an API as a waiter in a restaurant: you place an
order (a request), the waiter relays it to the kitchen (another
system), and then delivers the food (the response) back to you.
APIs are essential for AI agents because they enable seamless
integration with the tools they use—whether it’s accessing
databases, retrieving real-time information, or connecting with

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 Agentic Artificial Intelligence

cloud services. In most cases, without APIs, AI agents would be

isolated and unable to interact with tools.

Deterministic vs. Probabilistic Systems

Depending on how they process inputs and produce outputs, AI
systems can be classified as deterministic or probabilistic.
Deterministic systems always produce the same output
for a given input. They follow strict rules and logic, ensuring
predictable, repeatable results. Think of a basic calculator—
when you input “2 + 2,” you always get “4.” These systems
are ideal for tasks where precision and consistency are crucial,
like financial calculations or compliance checks. Deterministic
systems were used exclusively in the “expert systems” of early
AI, and are still present in robotic process automation systems,
clinical decision support systems in hospitals, and a surprising
number of other settings.
Probabilistic systems, on the other hand, operate based
on likelihoods rather than fixed rules. They analyze patterns
and data to make predictions, meaning their outputs can vary
slightly based on probabilities. Most AI models, including large
language models and recommendation systems, fall into this
category.11 They don’t provide guaranteed answers but instead
generate responses based on the most statistically probable
outcome. For example, when a chatbot predicts the next word in
a sentence, it selects the option with the highest likelihood rather
than following a rigid rule.

11
Thomas H. Davenport and Peter High, 2024. “How Analytical AI and Gen
AI Differ—and When to Use Each,” Harvard Business Review, https://hbr.
org/2024/12/how-gen-ai-and-analytical-ai-differ-and-when-to-use-each

32
 PART 1
THE RISE OF AI
AGENTS
This page intentionally left blank
CHAPTER 1
BEYOND CHATGPT: THE
NEXT EVOLUTION OF AI

T
he introduction highlighted a critical gap in our current
approach to AI—brilliant systems that can think but can’t
act. Now, let’s explore how we arrived at this pivotal
moment. In this chapter, we’ll trace the technological evolution
that made AI agents possible—the convergence of two powerful
streams that, when combined, created something greater than
either could achieve alone. Understanding this history isn’t just
academic; it reveals why the shift to agentic AI represents such
a profound opportunity.

The Birth of Agentic AI: A Convergence of

Powers
Picture yourself in a meeting room with a group of business
leaders. Someone inevitably asks the question we’ve heard
hundreds of times: “If AI is so smart, why can’t it just figure out
what needs to be done and do it?”
This question gets to the heart of what’s missing in today’s
AI landscape. To understand why this capability has been so
elusive—and how it’s finally becoming possible—we need to

35
 Agentic Artificial Intelligence

explore how distinct technological streams have converged to

create something entirely new: agentic AI.
Agentic AI isn’t the result of just one innovation. It’s a fusion
of multiple advancements—from voice assistants to self-driving
technology to AI-driven APIs. However, two technological
streams stand out as the most critical in making agentic AI a
reality:

• The rise of large language models.

• The evolution of workflow automation, now known as
intelligent automation.

A Tale of Two Technologies

The story of agentic AI isn’t a simple, linear progression.
Instead, it’s more like watching two rivers flow separately for
miles before finally meeting to form something more powerful
than either could be alone.
Let’s start with a story that illustrates why this convergence
matters. In 2022, we were working with a global manufacturing
company that was struggling with customer service efficiency.
They had already implemented an advanced chatbot powered
by a large language model that could understand and respond
to customer queries with remarkable accuracy. They had also
deployed robotic process automation (RPA) bots that could
execute complex sequences of actions in their backend systems.
Yet something was missing—the bridge between understanding
and doing.
Their customer service representatives still had to act as
human bridges, taking the chatbot’s recommendations and
manually triggering the appropriate automated workflows. It
was a glimpse of what was possible if these technologies could
work together directly, and it helped us understand why the
convergence of these technologies would be so transformative.

36
 Beyond ChatGPT: The Next Evolution Of AI

The First Stream: The Path to Large Language

Models
The journey of AI that led to today’s language models began
in 1997, and we were there to witness it. The world watched in
amazement as IBM’s Deep Blue defeated chess champion Garry
Kasparov. We remember the headlines: “Machine Beats Man!”
But here’s what most people missed—Deep Blue was more like
a savant than a genius. It could play chess brilliantly but couldn’t
even explain its own moves. Try asking it to play checkers, and
you’d have better luck teaching a fish to ride a bicycle.
This limitation bothered us and many others in the field.
Surely, we thought, there must be a better way to create intelligent
systems. The breakthrough came from an unexpected direction—
neural networks. Now, when we explain neural networks, we
like to use a simple analogy: imagine teaching a child about
animals. You don’t start by giving them a rulebook about fur,
tails, and the number of legs. Instead, you show them examples:
“This is a dog. This is a cat. This is a bird.” The child’s brain
naturally learns to recognize patterns and make generalizations.

The Neural Network Revolution

The beauty of neural networks is that they learn in a similar way.
But they needed three ingredients to reach their full potential:
vast amounts of data (think millions of examples), significant
computing power (imagine thousands of high-end computers
working together), and sophisticated architectures (the clever
ways we organize these artificial brain cells).
We remember the excitement in the AI community when
these elements finally came together in the 2010s. It was like
watching the first flight of the Wright brothers—suddenly,
something that seemed impossible became reality. Systems could
recognize images, understand speech, and process language with
unprecedented accuracy.

37
 Agentic Artificial Intelligence

The Emergence of Language Models

But the real magic happened in language processing. Let us take
you back to the old days of language AI—it was like trying to teach
a computer to understand Shakespeare by giving it a dictionary
and a grammar book. The results were about as wooden as you’d
expect.
Then came 2017, and with it, a breakthrough that changed
everything: the transformer architecture. Imagine giving an
AI not just the ability to look up words but also to understand
context, grasp meaning, and see how ideas connect. It was like
upgrading from a pocket calculator to a mathematician’s brain.
The scaling laws we discovered during this period still amaze
us. As these models grew larger and were trained on more data,
something magical happened—they developed abilities nobody
had programmed into them. It was like watching evolution
happen in fast-forward. GPT-3, released in 2020, shocked us.
Here was a system that could write code, solve math problems,
and even engage in philosophical discussions—tasks it was
never explicitly trained to do.
When ChatGPT arrived in 2022, it felt like reaching the
summit of a mountain we’d been climbing for decades. Finally,
we had an AI that could engage in genuine dialogue, reason
through problems, and explain its thinking in ways that made
sense to humans. But there was still one crucial limitation—it
could only suggest actions, not take them.

The Second Stream: The Evolution of

Automation
While all this was happening in the AI world, another revolution
was quietly unfolding in the realm of automation. We’ve had
a front-row seat to this evolution, watching it transform from
simple screen-scraping tools to sophisticated digital workers.

38
 Beyond ChatGPT: The Next Evolution Of AI

The Rise of Robotic Process Automation

In the early 2000s, much of business was still manual or relied
on disparate IT systems that didn’t talk to each other. Forward-
thinking teams started using scripts and macros to automate
repetitive computer tasks—for example, a macro to copy data
from an Excel file into a mainframe application every night.
In the early 2010s, we actively participated in the birth of
Robotic Process Automation (RPA). Think of it as software
tools that mimic the actions of a human on a computer: clicking,
typing, copy-pasting, and reading screens. It sounds simple, but
it was revolutionary. For the first time, we could have computers
work alongside humans, using the same tools and interfaces we
use.
Essentially, an RPA “robot” is a piece of software programmed
to follow a series of steps—log into a system, retrieve some
data, perform a calculation, and input results elsewhere—just
like a human would, but faster and without fatigue. RPA became
popular because it targeted low-hanging fruit: all those mundane,
rules-based tasks that office workers repeatedly do.
Businesses eagerly embraced RPA to reduce errors and free
employees from drudgery. We saw banks, insurers, and hospitals
deploy RPA bots to handle activities like data entry, invoice
processing, report generation, and database updating. For
instance, one insurance client of ours used RPA to automatically
transfer policy data from emails into their legacy system—what
used to take a team of people all day now happens reliably in
minutes.
However, these early RPA solutions had limitations. They
were fragile—if a single screen changed or an exception occurred
(like a missing field), the robot would get confused. RPA robots
had no intelligence or judgment; they strictly followed the script.
We often had to step in and update the bot or handle edge cases
manually. In essence, RPA was process-driven automation, good

39
 Agentic Artificial Intelligence

for structured tasks with clear rules, but not adaptable when
things deviate from the norm.

The Evolution to Intelligent Automation

The next step was even more exciting—combining RPA with
machine learning—what we named intelligent automation or
hyperautomation. To stay competitive, RPA tools started adding AI
capabilities so that they could handle more complex, unstructured
work​. Think of it this way: RPA is great at doing tasks, but it
lacks any thinking. So, companies began to augment RPA with
AI technologies—machine learning, natural language processing
(NLP), and computer vision—to create automation that could
interpret information and make simple decisions.
In practical terms, this meant an automated workflow could,
for example, read an email from a customer (using NLP), decide
what the request is about (using an AI classifier), and then trigger
the appropriate RPA process to handle it. The automation was no
longer blind and rigid; it became context-aware to a degree.
As automation grew more capable, we started aiming beyond
single tasks toward end-to-end process automation. Instead of
just automating steps in isolation, the goal became to automate
an entire workflow or business process from start to finish.
For example, for a retail company, we helped automate the
order-to-cash process: From receiving an online order, verifying
inventory, processing payment, and scheduling shipment to
updating the finance records. Multiple RPA robots, interfaces,
and AI models worked in concert, passing tasks along like
an assembly line, with humans only monitoring or handling
exceptions. When done right, the entire process runs on its own.
By the early 2020s, many businesses had achieved a high
degree of automation in routine processes. We observed what
we call the automation plateau: most of the straightforward
tasks were already automated, and the bottleneck became the
decision points and dynamic situations that still needed a human
in the loop. Traditional automation could go no further because

40
 Beyond ChatGPT: The Next Evolution Of AI

it lacked the adaptability and higher-level reasoning required

beyond well-defined rules.
And now, we arrive at the present moment—the convergence
of these two powerful streams. It’s like watching two puzzle
pieces finally click together. Language models provide the
brain—the ability to understand, reason, and plan. Automation
technologies provide the hands—the ability to execute actions
in the real world. When both converge, we get agentic AI—in
essence, intelligent digital workers.
This combination is what makes agentic AI possible, and
we’re thrilled to be part of this revolution. We’re watching AI
systems evolve from passive tools into active partners that can
both understand what needs to be done and actually do it.

Birth of the First LLM-based AI Agents

AI research and development in the last few years have rapidly
advanced the capabilities of AI agents. LLMs like GPT-3 and
GPT-4, which initially functioned as sophisticated prediction
engines for text, are now being augmented with the ability to
plan actions and use tools. This means instead of just completing
a sentence, they can decide to perform a web search, execute a
calculation, call an API, or invoke another piece of software as
part of answering a question or accomplishing a task.
One of the first LLM-based AI agent frameworks was
MRKL (Modular Reasoning, Knowledge, and Language) in
2022.12 It focused on modular reasoning, where LLMs interact
with predefined tools like “Search” and “Lookup” to retrieve
information and answer queries. The framework separated
reasoning from acting, relying on external modules for discrete
reasoning tasks.

12
Ehud Karpas et al., 2022. “MRKL Systems: A modular, neuro-symbolic
architecture that combines large language models, external knowledge
sources and discrete reasoning,” https://arxiv.org/abs/2205.00445

41
 Agentic Artificial Intelligence

The field evolved rapidly then with the introduction of

ReAct,13 which brought the concept further by enabling AI to
intermix reasoning steps with actions. The model generated a
thought process (a reasoning trace) and could take an action like
querying a database or using an API, then continued reasoning
with the new information​. This synergy between reasoning and
acting helped the AI adjust its plan on the fly and handle more
complex tasks. In experiments, ReAct greatly reduced the AI’s
tendency to hallucinate incorrect facts by letting it check a
source (like a Wikipedia API) before answering​. It also made the
AI’s decision process more transparent and interpretable, since
you could follow its step-by-step reasoning​.14
Toolformer (2023) was a breakthrough AI model that taught
itself to use external tools like calculators, web search, and
translators. This addressed a key weakness of large language
models, which struggled with arithmetic and real-time facts due
to their reliance on static training data. By deciding when and
how to call external tools, Toolformer enhanced accuracy in
calculations and question-answering.15
These advances have led to the emergence of highly capable
AI agents that can handle multi-step tasks. For example, you
might have heard of experimental systems like AutoGPT16
or BabyAGI,17 which gained popularity among enthusiasts.
These are essentially wrapper agents around LLMs designed to
autonomously pursue goals: they take a high-level goal from a

13
Shunyu Yao et al., 2022. “ReAct: Synergizing Reasoning and Acting in
Language Models,” https://arxiv.org/abs/2210.03629v3
14
Shunyu Yao et al., 2022. “ReAct: Synergizing Reasoning and Acting in
Language Models,” https://arxiv.org/abs/2210.03629v3
15
Timo Schick et al., 2023. “Toolformer: Language Models Can Teach
Themselves to Use Tools,” https://arxiv.org/abs/2302.04761
16
Wikipedia contributors, 2025. “AutoGPT,” https://en.wikipedia.org/wiki/
AutoGPT
17
Yohei Nakajima, 2024. “Impact of BabyAGI,” yoheinakajima.com, https://
yoheinakajima.com/impact-of-babyagi/

42
 Beyond ChatGPT: The Next Evolution Of AI

user and then generate sub-tasks, execute them (often by issuing

tool commands or even writing and running code), check the
results, and iterate until the goal is achieved. While these are
cutting-edge experiments and sometimes brittle (prone to getting
confused or stuck), they illustrate the direction of agentic AI.
In parallel, frameworks like LangChain18 and Semantic Kernel19
emerged to enhance these capabilities, making it easier for LLMs to
interact with external APIs, databases, and other systems. Suddenly,
agents weren’t just working in isolation—they were connecting
with the digital world, performing tasks like automating workflows,
retrieving information, or controlling applications.
The invention of function calling within LLMs pushed
this even further.20 It allowed agents to interact precisely with
external systems by running specific functions or scripts as part
of their reasoning process. This breakthrough meant agents
could not only plan and think but also act in highly targeted and
efficient ways.
The research reinforced this evolution. Papers like Gorilla21
demonstrated how models could learn to use tools effectively,
while studies from Microsoft,22 Stanford,23 and Tencent24
revealed that collaborative agents—multiple agents working
together—achieved even greater success.

18
LangChain, 2025. «Introduction,» https://python.langchain.com/docs/
introduction/
19
Microsoft, 2024. “semantic-kernel,” GitHub, https://github.com/microsoft/
semantic-kernel
20
OpenAI, 2024. “Function calling,” OpenAI Platform Documentation,
https://platform.openai.com/docs/guides/function-calling
21
Shishir G. Patil et al., 2023. “Gorilla: Large Language Model Connected
with Massive APIs,” https://arxiv.org/abs/2305.15334
22
Microsoft, 2024. “AutoGen,” Microsoft Research, https://www.microsoft.
com/en-us/research/project/autogen/
23
Joon Sung Park et al., 2023. “Generative Agents: Interactive Simulacra of
Human Behavior,” https://arxiv.org/abs/2304.03442
24
Deheng Ye et al., 2024. “More Agents Is All You Need,” https://arxiv.org/
abs/2402.05120

43
 Agentic Artificial Intelligence

By 2023, the rise of platforms available to enterprises like

AutoGen,25 Google Cloud Vertex AI,26 and CrewAI27 took
these concepts mainstream, creating environments where
LLM-driven agents could thrive. By incorporating automation
capabilities, LLMs have transformed from mere text processors
into the foundation of systems capable of reshaping how tasks
are performed, how decisions are made, and how technology
interacts with the world.

The Booming Landscape: Today’s AI Agent

Market
Market projections indicate that the market will grow at an
incredible 44% per year by 2030.28 We’re thrilled to see the
agentic AI market booming because it confirms what we’ve
long believed—AI agents aren’t just a passing trend; they’re the
future of how businesses and individuals will operate.
The fact that Gartner projects that one in three enterprise
software applications will integrate agentic AI by 2028 also signals
a fundamental shift in how companies approach automation,
decision-making, and productivity.29 But what excites us most is

25
Microsoft, 2024. “AutoGen,” GitHub, https://microsoft.github.io/autogen/
stable/
26
The Batch, 2024. “All About Google’s Vertex AI Agent Builder,”
deeplearning.ai, https://www.deeplearning.ai/the-batch/all-about-googles-
vertex-ai-agent-builder/
27
CrewAI, 2024. “CrewAI Launches Multi-Agentic Platform to Deliver
on the Promise of Generative AI for Enterprise,” GlobeNewswire, https://
www.globenewswire.com/news-release/2024/10/22/2966872/0/en/
CrewAI-Launches-Multi-Agentic-Platform-to-Deliver-on-the-Promise-of-
Generative-AI-for-Enterprise.html
28
Statista, 2025. “Market value of agentic artificial intelligence (AI)
worldwide 2024 with a forecast for 2030 (in billion U.S. dollars),” Statista,
https://www.statista.com/statistics/1552183/global-agentic-ai-market-value/
29
Tom Coshow, 2024. “Intelligent Agent in AI,” Gartner, https://www.
gartner.com/en/articles/intelligent-agent-in-ai

44
 Beyond ChatGPT: The Next Evolution Of AI

Sequoia Capital’s projection that AI agents could address a $10

trillion market, combining both global services and software
markets.30
The agentic AI market is new, explosive, and highly
competitive.31 Hundreds of vendors are already offering agent
platforms, while both startups and major companies are racing
to develop AI agents across industries.32
Because this market is evolving rapidly in many directions,
it can be difficult to navigate. To make it easier to understand, we
like to break it down into three main categories:

• First are customizable platforms that let professionals

and organizations build their own agents. These platforms
can be used across business industries and functions.
They range from no-code solutions like Beam33 and
Relevance.ai34 to low-code platforms like UiPath35 and
Microsoft’s Agent Builder,36 Crew.ai,37 and ServiceNow38

30
Sonya Huang, et al., 2024. “Generative AI’s Act o1: The Reasoning Era
Begins,” Sequoia Capital, https://www.sequoiacap.com/article/generative-
ais-act-o1/
31
Lindsey Wilkinson, 2025. “Enterprises eye agentic AI despite readiness
gaps and security concerns,” CIO Dive, https://www.ciodive.com/news/
enterprise-AI-agent-agentic-autonomous-strategy-challenges/738172/
32
Nicole Deslandes, 2024. “2025 Informed: The Year of Agentic AI,”
TechInformed, https://techinformed.com/2025-informed-the-year-of-
agentic-ai/
33
Beam AI, 2025. “Hire Self-Learning AI Agents to Run Your Operations -
Agentic AI by Beam,” beam.ai, https://beam.ai/
34
Relevance AI, 2025. “Build teams of AI agents that deliver human-quality
work,” relevanceai.com, https://relevanceai.com/
35
UiPath, 2025. “Build a path to agentic automation with UiPath Agent
Builder,” uipath.com, https://www.uipath.com/product/agent-builder
36
Microsoft, 2025. “Overview of Copilot Studio agent builder,” Microsoft
Learn, https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/
copilot-studio-agent-builder
37
CrewAI, 2025. https://www.crew.ai
38
ServiceNow, 2025. “Virtual Agent,” https://www.servicenow.com/
products/virtual-agent.html

45
 Agentic Artificial Intelligence

to full programming frameworks like Langchain39 and

AutoGen.40 These tools let businesses create agents
tailored to their specific needs and processes.

• Second are the generalist agents, like OpenAI’s Opera-

tor,41 Anthropic’s Computer Use,42 and Google’s Project
Mariner.43 These are more versatile, capable of handling
a wide range of tasks and adapting to different contexts.
Think of them as intelligent digital assistants that seam-
lessly navigate multiple systems, understand complex
goals, and execute tasks directly through the screen—
no complex integrations required. In our view, the
“ChatGPT moment” of agentic AI will come from the
evolution of this category of agents, as they will become
widely available to both consumers and companies.

• Third are the specialist agents, like Google or OpenAI

Deep Research (specialized in research), Agentforce
(specialized in sales and customer relationships),44 or
Hippocratic AI (specialized in agents for healthcare).45
Some of these agents specialize in specific functions
across industries (horizontal agents), while others are
tailored to a single industry’s unique needs (vertical

39
Langchain, 2025. https://www.langchain.com/
40
AutogenAI, 2025. https://autogenai.com
41
OpenAI, 2025. “Introducing Operator,” https://openai.com/index/
introducing-operator/
42
Anthropic, 2025. “Computer Use,” https://docs.anthropic.com/en/docs/
build-with-claude/computer-use
43
Google DeepMind, 2024. “Project Mariner,” Google DeepMind, https://
deepmind.google/technologies/project-mariner/
44
Salesforce, 2025. “Agentforce,” Salesforce, https://www.salesforce.com/
agentforce/
45
Hippocratic AI, 2025. “Hippocratic AI,” https://www.hippocraticai.com/

46
 Beyond ChatGPT: The Next Evolution Of AI

agents).46 Most of these are ready-to-use agents focused on

specific tasks—whether it’s analyzing legal documents,
optimizing marketing campaigns, or conducting notarial
work. You can find hundreds of these specialized agents
on marketplaces like agent.ai,47 each designed to excel at
a particular function.

In the appendix, you’ll find a detailed breakdown of the

current market offerings, structured according to the 5-Level
Progression Framework introduced in Chapter 2.
In addition, we understand that navigating this market can be
challenging, especially when choosing the right agent platform
to start your agentic journey. That’s why, in Chapter 8, we not
only guide you through building agents but also help you select
the best platform to match your needs and objectives.

Agentic AI for Entrepreneurship

and Business
The AI agents we’re discussing don’t just think for us; they also
act for us. Over the last couple of years, we’ve helped develop
and deploy some of these AI agents, and working with these
agents truly feels like collaborating with a new kind of colleague.
Let’s unpack what this means in practice.

Introducing Your Intelligent Digital Workers

An intelligent digital worker is like a virtual employee who can
handle a process autonomously. It’s “hired” to perform a job,
such as an IT support agent, a customer service representative,

46
Larry Dignan, 2024. “Agentic AI: Three Themes to Watch in 2025,”
Constellation Research, https://www.constellationr.com/blog-news/insights/
agentic-ai-three-themes-watch-2025
47
Agent.ai, 2025. https://agent.ai/

47
 Agentic Artificial Intelligence

or a marketing assistant—only it’s made of code. What enables

it to do the job is the combination of an LLM’s cognitive abilities
with the direct action capabilities of automation software. The
LLM allows the agent to interpret instructions, converse in
natural language, and make reasoned decisions. The automation
side empowers it to execute actions: clicking buttons, retrieving
and entering data, calling APIs, orchestrating other software
tools, etc. Together, these let the AI agent handle complex tasks
end-to-end.
In practical terms, here’s how an AI agent works: Suppose
you give an AI agent a high-level goal like, “Update our social
media to respond to the latest product launch feedback.” A
language-capable agent will first understand the request (maybe
asking us for clarification if needed, just as a human would).
Then, it will break down the goal into actionable steps: e.g., 1)
Gather recent customer feedback from social media and reviews,
2) Analyze sentiment (positive vs. negative themes), 3) Draft
responses or a PR message addressing concerns, 4) Post the
responses or schedule a blog update, and 5) Monitor reactions.
In a traditional setting, this might involve a team of people
across marketing and customer support coordinating these steps.
An AI agent, however, can coordinate them on its own: using
integration hooks to pull data from Twitter or Facebook, running
an NLP sentiment analysis model, generating text through
its LLM, and interfacing with the company’s social media
management tool to publish updates.
Throughout, the agent keeps track of context—it knows
what the overall objective is and adjusts its actions if something
unexpected happens (for example, if it finds an unusually critical
tweet, it might escalate that specific issue to a human).

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 Beyond ChatGPT: The Next Evolution Of AI

AI Agents in Action: Real-World

Transformations
We have implemented firsthand examples across industries.
Let’s look at a few real-world use cases of intelligent digital
workers in action.
Take customer service, one of the most immediate and
visible applications of AI agents. Gone are the days of rigid,
scripted chatbots that frustrate users. Today’s AI agents hold
full conversations, diagnose issues, and take action to resolve
them.48 We worked with a telecom company to deploy an AI-
driven support agent capable of handling common tech support
calls from start to finish.
A customer calls in with an internet issue—the agent listens,
transcribes their words, and understands the problem using a
large language model. Then, it troubleshoots: it remotely checks
the modem, walks the customer through a reset if needed,
and, if the issue persists, creates a service ticket or schedules a
technician. By the end of the interaction, the problem is either
resolved or seamlessly escalated, without human intervention.
The AI doesn’t just provide answers—it takes action, managing
the full lifecycle of a customer request.
Now, imagine this level of intelligence applied to finance and
accounting. We helped a global finance team introduce a “digital
analyst agent” that automated their monthly budget variance
analysis. Instead of human analysts spending days sifting
through numbers, this AI agent pulled data from the accounting
system, identified where spending deviated from the plan, and
even drafted explanations using natural language models. When

48
See recent research on AI Concierges and how they are expected to
transform customer journeys: Liu, S.Q., Vakeel, K.A., Smith, N.A., Alavipour,
R.S., Wei, C.(V). and Wirtz, J., 2024. “AI concierge in the customer journey:
what is it and how can it add value to the customer?”, Journal of Service
Management. https://doi.org/10.1108/JOSM-12-2023-0523

49
 Agentic Artificial Intelligence

it spotted anomalies—such as an unusual expense—it flagged

them for human review.
Over time, by learning from feedback, the AI improved
its ability to distinguish between normal fluctuations and true
red flags. What once required hours of manual effort became a
streamlined process, with the AI handling the heavy lifting and
humans stepping in only when necessary. The result? A system
where financial reporting became faster, more precise, and free
of tedious manual work—like having a junior analyst who never
sleeps, continuously crunching numbers and preparing insights.
In Chapter 12, we take a deep dive into real-world case studies
and use cases across industries. You’ll see how organizations are
solving complex challenges, enhancing customer experiences,
and redefining the future of work with AI agents.

Opportunities and Challenges for Companies

For business leaders and decision-makers, the rise of agentic AI
is both an opportunity and a challenge. On the opportunity side,
the benefits are tantalizing. We can achieve massive efficiency
gains by offloading work to AI agents—imagine doubling your
workforce without doubling headcount, as mundane tasks are
handled by tireless digital workers. We also foresee improvements
in quality and consistency.
An AI agent, when properly trained and governed, will follow
best practices every single time, and it can embed compliance
checks into every action (reducing the risk of human error).
Perhaps most interestingly, agentic AI can unlock new capabilities
that weren’t feasible before. For example, you might deploy 20
AI agents to simulate various market scenarios overnight and
have strategic recommendations by morning—a scale of analysis
no human team could do in time. Or provide truly personalized

50
 Beyond ChatGPT: The Next Evolution Of AI

service to each of thousands of customers simultaneously through

AI concierges.
In fields like customer service and decision support, this
means customers get faster responses, and executives get deeper
insights for decision-making. AI agents work alongside humans
as collaborators—think of it as each employee having an AI
copilot that can take on tasks or provide suggestions, dramatically
amplifying what that employee can accomplish in a day.
On the challenge side, we must navigate the transition
carefully. Integrating AI agents into workflows requires careful
change management. Work must be intentionally designed
to allow optimal human and AI collaboration, ensuring that
employees trust and understand their new AI teammates. Part
of our job in deployments has been demystifying how the agent
makes decisions (adding explanations or “audit trails” of AI
decisions) so that people feel comfortable.
In addition, building AI agents comes with several technical
challenges that go beyond just development. Agent reliability is one
of the biggest hurdles—ensuring that agents consistently perform as
expected without errors or unintended behaviors. Defining precise
goals and instructions is equally complex, requiring constant
iteration to refine responses and decision-making.
Seamless integration is another key factor; AI agents must
connect effortlessly with existing tools, APIs, and workflows
to be truly effective. Data quality also plays a crucial role—
garbage in leads to garbage out, making agents unreliable, error-
prone, and incapable of accurate decision-making. Ultimately,
successful deployment isn’t just about building agents—it’s
about continuously integrating, testing, and refining them to
ensure they deliver real-world value.
There’s also the question of oversight: AI agents need
guardrails and ethical guidelines. They are powerful, but they

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 Agentic Artificial Intelligence

should still align with business rules, values, and regulatory

requirements. Collaboration between humans and AI will only
work if humans ultimately remain in control of setting goals and
reviewing critical decisions—especially in areas like finance or
healthcare where stakes are high.
Fortunately, tools for AI governance are improving, and we
always stress a human-in-the-loop approach, especially in the
early stages of using agentic AI. Think of the AI agent as a very
capable new hire—you wouldn’t let a new employee run wild
without training and oversight, and the same goes for AI.
As we move forward, it’s important to remember that agentic
AI is augmentative, not purely replacement. The ultimate model
is AI + Human working in tandem. The AI handles the heavy
lifting and routine grind while humans provide guidance,
creativity, and critical oversight. In one successful deployment,
a client told us their human team members started calling the AI
agent “a member of the team”—that’s the kind of collaborative
mindset that leads to the best outcomes.
We’re entering a future where your next new “employee”
might just be an AI. Embracing this future and shaping it
responsibly will be a key theme for businesses in the years to
come. It’s an exciting time to be part of this journey.
To explore this further, Part 4 of the book serves as your
strategic playbook for business transformation in the age of AI
agents. You’ll find real-world use cases, case studies, and the
key ingredients for successful organizational change—including
strategy, governance, change management, and value creation.

Startups: The Biggest Winners in the AI Agent

Era
Just as e-commerce, social media, and SaaS reshaped industries,
AI agents will unlock entirely new business models, creating

52
 Beyond ChatGPT: The Next Evolution Of AI

opportunities for entrepreneurs to be the first movers in

emerging markets. We believe startups stand to gain the most
from AI agents’ automation, and the numbers prove it.49 They
make up the largest share of companies leveraging AI agents—
for example, over 50% of businesses using the LangChain agent
platform have fewer than 100 employees, while large enterprises
with over 10,000 employees account for barely 15%.50
Unlike large enterprises, which are more tied to bureaucracy
and legacy systems, startups can build their entire business
models around AI agents from day one. This gives them a
massive competitive edge by enabling:

• Cost-efficient operations—Automating core tasks

reduces the need for large teams.
• Agility and rapid prototyping—AI agents enable faster
market entry and iteration.
• Personalized customer interactions at scale—AI agents
allow startups to outmaneuver larger competitors, which
rely on impersonal, generalized approaches.
• Freedom from integration constraints—Unlike big com-
panies, startups don’t have to retrofit AI into workflows
that need to be updated.

This agility allows startups to carve out niche markets, offer

hyper-customized solutions, and thrive in underserved sectors.
As AI agents evolve beyond simple automation into autonomous
decision-makers, they will become the backbone of the next
wave of disruptive businesses. Examples of startups that have

49
Hayden Field, 2024. “After ChatGPT and the rise of chatbots, investors
pour into AI agents,” CNBC, https://www.cnbc.com/2024/06/07/after-
chatgpt-and-the-rise-of-chatbots-investors-pour-into-ai-agents.html
50
LangChain, 2024. “The State of AI Agents,” https://www.langchain.com/
stateofaiagents

53
 Agentic Artificial Intelligence

been able to seize these opportunities are Perplexity,51 Ramp,52

Superhuman,53 or Replit.54
To explore this topic further, Chapter 9, “From Ideas to In-
come,” reveals how entrepreneurs are already building specialized
AI agents in industries like healthcare, finance, and logistics—and
how you can do the same. This chapter also offers a proven frame-
work for identifying high-value agentic business opportunities
and transforming them into profitable ventures.

The State of AI Agent Adoption in

Companies
To understand how organizations are implementing and benefiting
from AI agents, we conducted an extensive analysis of companies
that have moved beyond rule-based agents and the conventional
use of LLMs to deploy LLM-based AI agents. Our research
team collected and analyzed data from 167 companies across
various sectors that have implemented such agents in production
environments. The study focused on understanding implementation
patterns, challenges faced, benefits realized, and key success factors.

51
LangChain, 2024. “Perplexity: An AI answer engine that lets you
handle complex query searches like a Pro,” https://www.langchain.com/
breakoutagents/perplexity
52
LangChain, 2024. “Building an AI tour guide that helps users navigate
Ramp’s platform for financial operations,” https://www.langchain.com/
breakoutagents/ramp
53
LangChain, 2024. “Superhuman: Navigate your inbox and calendar
in a flash, with an AI-powered search assistant for emails,” https://www.
langchain.com/breakoutagents/superhuman
54
LangChain, 2024. “Transforming how users build software from scratch,
to code, to application with Replit Agent,” https://www.langchain.com/
breakoutagents/replit

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 Beyond ChatGPT: The Next Evolution Of AI

Current State of Implementation

The landscape of AI agent adoption reveals fascinating patterns
across industries. Technology and software companies are
leading the charge, representing nearly a quarter of successful
implementations in our study. This isn’t surprising, given their
technical capabilities and appetite for innovation. Financial
services follow closely at 18%, with retail completing the top
three at 16% of implementations.

Sector Percentage Notable Examples

Technology & Software 24% Microsoft, Salesforce, BMC
Financial Services 18% JPMorgan, ING Bank, Klarna
Retail & Consumer 16% Best Buy, Lowe’s,
McDonald’s, Pets at Home
Healthcare & Life 12% HCA Healthcare, Hackensack
Sciences Meridian
Professional Services 10% McKinsey, Accenture,
Thomson Reuters
Travel & Hospitality 8% IHG Hotels, Alaska Airlines,
HomeToGo
Manufacturing & 6% Continental, Fortenova Group
Industrial
Other 6% Various

Table 1.1: Industry distribution of companies implementing AI agents

according to our research (Source: © Bornet et al.)

These early adopters aren’t merely experimenting—they’re

achieving remarkable results. Microsoft, for instance, reported a
9.4% increase in revenue per seller and 20% more closed deals
using AI agents in their sales function. In the financial sector,
JPMorgan deployed agents that reduced fraud by an impressive
70%. These success stories are encouraging more companies to
explore similar implementations.

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 Agentic Artificial Intelligence

The data reveals five primary categories where organizations

are successfully deploying agents. Customer service and support
lead the pack, accounting for 35% of implementations. These
systems handle everything from automated query resolution to
personalized service delivery, with companies reporting average
resolution time improvements between 12% and 30%.
Internal operations follow at 25%, with organizations using
agents to handle document processing, workflow optimization,
and administrative tasks. The results here are particularly
striking, with time savings ranging from 30% to 90% for certain
processes. McKinsey & Company’s client onboarding agent, for
example, demonstrated a 90% reduction in lead time and 30%
reduction in administrative work.

Use Case Category Percentage Key Benefits Reported

• 12-30% faster resolution times
Customer Service & • 20-40% reduction in support costs
35%
Support • Higher customer satisfaction
scores
• 30-90% reduction in processing
time
Internal Operations 25%
• 25-50% cost savings
• Reduced error rates
• 9-21% revenue increase
Sales & Marketing 20% • 20-30% more deals closed
• Higher conversion rates
• 70% fraud reduction
Security & Fraud
12% • Faster threat detection
Detection
• Improved accuracy
• Industry-specific improvements
Specialized Industry
8% • Regulatory compliance
Solutions
• Enhanced service delivery

Table 1.2: AI agent use cases and key business impacts according to our
research (Source: © Bornet et al.)

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 Beyond ChatGPT: The Next Evolution Of AI

Sales and marketing applications represent 20% of im-

plementations, focusing on lead qualification, campaign op-
timization, and personalized outreach. Companies in this
category report revenue increases ranging from 9% to 21%.
Security and fraud detection accounts for 12% of implemen-
tations, while specialized industry solutions make up the re-
maining 8%.

Measuring Agent Impact

The quantifiable improvements reported by organizations in our
study are substantial. In terms of efficiency gains, companies
consistently report process time reductions between 30% and
50%, cost savings of 25% to 40%, and error reduction rates of
30% to 60%. Revenue impact is equally impressive, with sales
increases ranging from 9% to 20%, conversion rate improvements
of 15% to 25%, and customer satisfaction increases of 20% to
40%.
A particularly interesting finding is that 70% of successful
implementations use hybrid human-AI workflows. These
organizations have found that maintaining human oversight for
critical decisions while leveraging AI for routine tasks not only
improves results but also increases employee satisfaction. This
approach enables continuous improvement as humans and AI
agents learn from each other.

Blueprint for Success: Winning Agent

Implementation Strategies
Our research reveals that successful implementations typically
follow a measured, strategic approach. Nearly two-thirds of
successful implementations began with pilot programs, allowing
organizations to test and refine agent capabilities in a controlled
environment while building internal support through demonstrated
success.

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 Agentic Artificial Intelligence

The data points to three critical success factors that consistently

appear across successful implementations. First is the clear definition
of use cases. Organizations that succeed take time to identify
specific problems they want to solve and establish measurable
success metrics before deployment. They maintain well-defined
scope limitations and implement regular performance evaluations
to ensure the agents continue to meet their objectives.
The second is strong change management. Companies that
excel in this area develop comprehensive employee training
programs and maintain clear communication channels throughout
the implementation process. They recognize that successful agent
deployment is as much about people as it is about technology.
The third is robust technical infrastructure. Successful organi-
zations ensure they have integrated data architecture, strong secu-
rity measures, and scalable systems in place before deployment.
They also maintain regular maintenance and update schedules to
keep their systems running optimally.
We’ll dive deeper into these key success factors later in the
book, with detailed strategies for designing effective AI agents
(Chapter 8) and successfully integrating them into your business
(Part 4).

How Leaders Overcome Agent Adoption

Hurdles
Organizations implementing AI agents face several common
challenges. System integration is the most frequently cited,
with 45% of companies reporting it as a significant hurdle.
Successful organizations address this through phased integration
approaches and API-first architecture, along with regular testing
and validation procedures.
Data quality presents another major challenge, reported by 35%
of companies. Organizations overcome this through structured data
governance initiatives and regular quality assessments. Technical
expertise rounds out the top three challenges, with 20% of respon-

58
 Beyond ChatGPT: The Next Evolution Of AI

dents citing it as a concern. Leading organizations address this

through comprehensive training programs and strategic external
partnerships.
Operational challenges center around change management,
performance monitoring, and cost management. Successful or-
ganizations tackle these through clear communication strate-
gies, defined KPIs, and regular cost reviews. They also maintain
strong feedback loops to ensure continuous improvement.
We’ll take a deeper look at these critical challenges and solu-
tions later in the book, offering practical strategies for designing
powerful AI agents (Chapter 8) and effectively implementing
them into your business (Part 4).

Where Agents Are Headed

and How to Prepare
Looking ahead, the trajectory of AI agent adoption shows no
signs of slowing. Our research indicates that 85% of companies
plan to increase their agent implementations, with seven out of
ten exploring new use cases and 60% increasing their AI budgets.
More than half are developing custom solutions tailored to their
specific needs.
Our research suggests several key recommendations for
business leaders considering or beginning their journey with AI
agents. First, while thinking strategically is important, starting
small is crucial. Successful organizations identify high-impact,
low-risk use cases for their initial implementations and build on
these successes incrementally.
Infrastructure investment should be a priority, with particular
attention paid to data quality and security measures. Organizations
need to ensure their systems are scalable to accommodate future
growth and additional use cases.
Change management deserves significant attention. Successful
organizations develop comprehensive training programs and create
clear communication channels. They work to foster a culture of

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 Agentic Artificial Intelligence

innovation where employees feel empowered to work alongside AI

agents rather than threatened by them.
Finally, measurement and iteration are crucial. Successful
organizations define clear success metrics from the start and
maintain regular performance reviews. They view their AI agent
implementations as continuous improvement cycles rather than
one-time deployments.

The Road Ahead: What Adoption Trends

Tell Us
The data from our study clearly demonstrates that AI agents
are delivering significant value across industries. However,
success requires a thoughtful approach combining technical
expertise, strong change management, and clear business
objectives. Organizations that follow the patterns of successful
implementations—starting with pilots, focusing on clear use
cases, and maintaining strong human oversight—are most likely
to achieve positive results.
As we look ahead, the trend toward increased agent
implementation shows no signs of slowing. Business leaders
should view this not as a threat but as an opportunity to reimagine
how work gets done in their organizations. The key is to approach
implementation strategically, learn from early adopters, and
maintain a balanced view of how agents can augment and
enhance human capabilities rather than replace them.
Later in the book, we’ll break down these implementation
key challenges and best practices, covering how to design AI
agents for maximum impact (Chapter 8) and integrate them
seamlessly into your business operations (Part 4).

60
CHAPTER 2
THE FIVE LEVELS OF
AI AGENTS: FROM
AUTOMATION TO
AUTONOMY

A
s we saw in Chapter 1, the market for AI agents is grow-
ing rapidly, with hundreds of vendors offering solutions
across a spectrum of capabilities. This proliferation cre-
ates a challenge: How do we make sense of these different sys-
tems? How do we distinguish between simple automation tools
and truly autonomous agents? This chapter introduces a com-
prehensive framework for understanding the progression of AI
agent capabilities—from basic rule-following to sophisticated
autonomy—that will help you navigate this complex landscape
and make informed decisions about which solutions are right for
your needs.

Breaking Down the AI Agent’s Capabilities

When we first started implementing AI agents in organizations,
we noticed a common pattern. Business leaders would often

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dive straight into deployment without truly understanding what

these digital teammates could and couldn’t do. It reminded us
of trying to work with a new colleague without first learning
about their skills, experience, and working style—a recipe for
misaligned expectations and missed opportunities.

Why Capability Mapping Matters

When integrating a new team member, we don’t simply hand
them tasks and hope for the best. We invest time in understanding
their capabilities, assessing their strengths and weaknesses, and
learning how to work together effectively. Through interviews,
discussions, and practical tests, we discover not just what they
can do but how they think, how they approach problems, and
where they might need support or guidance.
This same thoughtful approach is crucial when working
with AI agents. While these digital colleagues can process
information at incredible speed and scale, they also have their
own unique characteristics, limitations, and ways of “thinking.”
Understanding these aspects isn’t just about knowing what
tasks to delegate—it’s about building effective partnerships that
maximize the potential of both human and artificial intelligence.

The SPAR Framework: A Natural Way to

Understand AI Agents
To help explain AI agent capabilities, we developed what
we call the SPAR framework: Sense, Plan, Act, and Reflect. We
chose this name deliberately—like a sparring partner in combat
sports, an AI agent constantly interacts with and adapts to its
environment.

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The Five Levels Of AI Agents: From Automation To Autonomy

Figure 1.1: How a Human Takes Action (Source: © Bornet et al.)55

This framework mirrors how we humans achieve our own

goals. We start by deciding what needs to be done—like cooking
dinner. Next, we gather input, checking what ingredients are
available. Then, we think through our options, choosing the
best approach—perhaps making spaghetti. Once decided, we
plan the steps, like boiling water and preparing the sauce. With
a clear plan, we take action and cook the meal. Afterward, we
evaluate the result, learn from the experience, and adjust for the
future—perhaps using less salt next time—creating a continuous
feedback loop for improvement.
When we explain AI agents to business leaders and
professionals, we often find ourselves drawing parallels to
autonomous vehicles. It’s not just because self-driving cars are
fascinating—they’re actually perfect examples of AI agents in
action. Through this lens, let’s explore the SPAR framework:
Sense, Plan, Act, and Reflect. This framework captures the four

55
Open Access: All figures in this book are distributed under the terms of
the Creative Commons Attribution 4.0 International License, which permits
unrestricted use, distribution, and reproduction under the conditions detailed
here: http://creativecommons.org/licenses/by/4.0/.

63
 Agentic Artificial Intelligence

fundamental capabilities that define how AI agents operate in

their environments.

Figure 1.2: How an AI agent takes action: The SPAR Framework (Source: ©
Bornet et al.)

Sensing: The Eyes and Ears of Agents

Imagine sitting in a self-driving car as it navigates through
city streets. The vehicle’s array of cameras, radar systems, and
sensors are constantly gathering data about its surroundings—
monitoring everything from the position of nearby vehicles to
traffic signals and road conditions. This is remarkably similar to
how AI agents operate in digital environments.
Just as a self-driving car needs to understand its environ-
ment comprehensively, AI agents must be able to perceive their
digital workspace. They gather data from multiple sources, de-
tect important triggers, and maintain awareness of their operat-
ing context. When you enter a destination into an autonomous
vehicle, you’re setting its goal—just like when you assign an
objective to an AI agent. The agent maintains what we call
a “short-term context window,” similar to how a self-driving

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The Five Levels Of AI Agents: From Automation To Autonomy

car keeps track of immediate road conditions and navigation

requirements.

Planning: Charting the Course

Once an autonomous vehicle knows where it needs to go,
it doesn’t just start driving blindly. It processes map data,
considers traffic patterns, and evaluates multiple possible routes.
This planning phase perfectly mirrors how AI agents work. They
don’t simply jump into execution—they first process available
information to make informed decisions about how to achieve
their objectives.
Think about how a self-driving car plans a lane change. It
doesn’t just swerve immediately; it evaluates the speed and position
of surrounding vehicles, calculates the optimal moment to move,
and ensures the maneuver can be completed safely. Similarly, AI
agents engage in sophisticated reasoning to develop step-by-step
plans for achieving their goals. They evaluate options, prioritize
actions, and coordinate resources, much like how an autonomous
vehicle coordinates its various systems to execute a complex
driving maneuver.

Acting: Putting Plans into Motion

The ability to take concrete action sets both autonomous vehicles
and AI agents apart from simple analytical systems. When a self-
driving car executes a turn, it coordinates multiple systems—
steering, acceleration, braking—in precise sequences. Similarly,
AI agents use their available tools to carry out actions in their
environment, whether that’s sending communications, updating
systems, or managing digital resources.
What’s particularly interesting is how both systems monitor
their actions in real-time. Just as a self-driving car continuously
adjusts its steering and speed based on road conditions, AI agents
actively monitor their actions for accuracy and effectiveness,

65
 Agentic Artificial Intelligence

making adjustments as needed to stay on course toward their

objectives.
When something goes wrong in an autonomous vehicle,
there is usually a remote human who can take over and resolve
the problem.56 Similarly, when AI agents take action, there needs
to be a clear path for humans to review those actions and take
remedial steps when necessary.

Reflecting: Learning from Experience

Perhaps the most sophisticated capability in both autonomous
vehicles and AI agents is their ability to learn and adapt from
experience. When a self-driving car encounters road construction
or heavy traffic, it doesn’t just navigate through the immediate
situation—it can incorporate this information into its knowledge
base to improve future journeys.
This reflective capability enables both systems to get better
over time. Just as autonomous vehicles learn optimal routes
and driving patterns, AI agents can evaluate their performance,
analyze outcomes, and refine their approaches based on what
works best. They build what we might call an “operational
memory” that helps them perform more effectively in similar
situations in the future.

The Power of Integration

What makes both self-driving cars and AI agents so powerful is
how these four capabilities work together in a continuous cycle.
Each capability feeds into and enhances the others, creating a
unified system that can pursue complex goals with increasing
sophistication. The car’s sensors inform its planning, which

56
Cade Metz, 2024. “When Self-Driving Cars Don’t Actually Drive
Themselves,” The New York Times, September 11, 2024, https://www.
nytimes.com/2024/09/11/insider/when-self-driving-cars-dont-actually-
drive-themselves.html

66
The Five Levels Of AI Agents: From Automation To Autonomy

guides its actions, which generate experiences that it learns

from—all while maintaining focus on the ultimate objective of
safe, efficient transportation.
This integrated approach represents a fundamental shift from
traditional automation. Rather than following rigid, predetermined
instructions, both autonomous vehicles and AI agents actively
engage with their environments, make decisions, take actions, and
learn from outcomes. They don’t just execute commands—they
work toward objectives with a degree of independence that makes
them true agents of change in their respective domains.
While the SPAR framework helps us understand what AI
agents can do, it doesn’t tell us how well they can do it. Think
about driving: just knowing that a vehicle needs steering,
acceleration, braking, and navigation doesn’t tell you whether
you’re dealing with a basic sedan or a fully autonomous vehicle.
Similarly, different AI agents can have vastly different levels of
sophistication in how they sense, plan, act, and reflect.
This complexity creates a challenge for organizations. When
vendors claim their solutions use “AI agents,” what exactly does
that mean? How can business leaders evaluate and compare
different systems? How do they know what level of capability
they actually need?

The Complex Reality of AI Agents’

Capabilities
However, through our consulting work and research, we’ve
found no industry consensus on what truly defines an “agent.”
To address this, we advocate for a progression framework—one
that reflects the evolutionary nature of AI capabilities. Just as
technology evolves from simple to sophisticated, this framework
provides a structured way to assess and define the advancing
role of AI agents.

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 Agentic Artificial Intelligence

The binary classification of “agent” or “not agent” is problematic

in the current AI landscape. Such rigid categorization fails to
capture the nuanced capabilities of different systems, often leads to
unrealistic expectations or underestimation of a system’s potential,
and doesn’t align with the incremental nature of AI development in
real-world applications.
We also hear voices limiting AI agents to only the most
sophisticated systems—those that act, learn, and adapt. We think
this is limiting and misleading. It is like saying a car isn’t a car
unless it’s fully autonomous. The reality is that progress is built in
stages. If we adopt a narrow definition, we miss the opportunities
unfolding right now with foundational AI agents already driving
impact. The question isn’t ‘Is it the ultimate agent?’ It’s ‘How
effectively can it act today—and what’s next?’ Let’s keep the
door open to innovation at every stage of the journey.
After exploring various frameworks with the companies
we help, we found that the automotive industry offers a perfect
analogy that resonates with both technical and business
stakeholders. Just as the Society of Automotive Engineers (SAE)
defines six levels of driving automation, from Level 0 (fully
manual) to Level 5 (fully autonomous under all conditions), we
can apply a similar progression path to AI agents.
Today, despite the impressive capabilities of cars like Tesla,
we’re mainly operating at Level 2 or 357--where automation
handles many tasks but still requires human oversight and
occasional intervention. At the time of printing the book (March
2025), Waymo and Cruise are just testing Level 4 autonomous
vehicles in limited, “geo-fenced” areas for ride-hailing services
in cities like Phoenix, San Francisco, and Los Angeles.58

57
Wikipedia contributors, 2025. “Tesla Autopilot,” https://en.wikipedia.org/
wiki/Tesla_Autopilot
58
Jameson Dow, 2024. “Waymo starts fully autonomous rides in LA
tomorrow; Austin later this year,” Electrek, https://electrek.co/2024/03/13/
waymo-starts-fully-autonomous-rides-in-la-tomorrow-austin-later-this-year/

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The Five Levels Of AI Agents: From Automation To Autonomy

The Agentic AI Progression Framework

The same is true for AI agents. While we often talk about them as
fully autonomous systems, in reality, we’re dealing with varying
levels of capability and independence that progress along a clear
developmental path.
At the early stages of this progression, we have AI agents
that can execute specific, predefined tasks but require significant
human oversight—like a car with basic driver assistance features.
As we move further along the Progression Framework, we find
agents that can handle more complex sequences of actions and
make some independent decisions but still need human validation
at critical points—similar to today’s most advanced commercial
vehicles. At the far end of this progression lie the highest levels,
where agents can fully understand, plan, and execute complex
missions with minimal human input across any domain. These
remain largely theoretical—just as Level 5 autonomous vehicles
are still a future goal.
Understanding these progression levels isn’t just an academic
exercise. This framework helps organizations detect overblown
claims about AI agent capabilities and make informed decisions
about AI integration in their projects. It enables more effective
communication between technical teams and end-users while
providing a clear roadmap for AI strategy development that cuts
through the hype.
Let’s explore what each of these progression levels looks
like in practice, starting with Level 0—Manual Operations,
where humans perform all cognitive and execution tasks without
any automation assistance. From there, we’ll see how each
level builds upon the previous one, adding new capabilities and
degrees of autonomy while still requiring appropriate human
oversight.

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 Level Car Analogy Agentic AI Main Technology SPAR Capabilities (Sensing, Planning, Acting,
Analogy Involved Reflecting)
Level 0 Manual Humans perform Basic digital tools NA
- Manual driving with no all tasks without (spreadsheets,
Operations assistance. automation. email), manual
(Human-Only) processing.
Level 1 - Basic cruise Simple automation Basic automation Sensing: Predefined triggers and structured data.

Agentic Artificial Intelligence

Rule-Based control follows fixed rules tools (RPA, simple Planning: Simple if-then rules and decision trees.
Automation maintains speed (e.g., data entry, scripts, rule Acting: Deterministic actions based on fixed inputs.
but needs human RPA systems). engines). Reflecting: No true learning, only logging and error
operation. reporting.
Level 2 - Advanced AI combines AI tools (machine Sensing: Semi-structured data from multiple sources.
70

Intelligent driver automation with learning, NLP, Planning: Basic AI models for pattern recognition and
Process assistance cognitive abilities computer vision, decision-making.
Automation systems handle like NLP and RPA, process Acting: Sophisticated actions with error handling.
speed and machine learning. orchestration). Reflecting: Basic analytics and performance
steering with monitoring, no adaptive capabilities.
supervision.
Level 3 - Vehicles Agents generate Large language Sensing: Advanced natural language understanding
Agentic navigate content, plan, models, memory and context awareness.
Workflows highways but reason, and systems, content Planning: Reasoning using foundation models,
need human adapt in defined generation tools, orchestrating complex workflows.
intervention domains. basic reinforcement Acting: Chaining tools and handling multi-step tasks.
in complex learning. Reflecting: Limited short-term feedback adjustments
situations. and long term memory.
 The Five Levels Of AI Agents: From Automation To Autonomy
Level Car Analogy Agentic AI Main Technology SPAR Capabilities (Sensing, Planning, Acting,
Analogy Involved Reflecting)
Level Self-driving Agents work Advanced reasoning Sensing: Multi-modal perception and interpretation of
4 - Semi- cars operate autonomously and planning, real- diverse inputs.
Autonomous autonomously within defined time adaptation, Planning: Dynamic strategies for complex tasks and
Agents in specific expertise, adapt causal reasoning. goal breakdown.
conditions. strategies, and Acting: Autonomous tool usage and error recovery.
learn. Reflecting: Retains context across sessions, learns
from past experiences.
Level 5 - Fully Fully AI systems handle Sophisticated Sensing: Complete environmental awareness and goal
Autonomous autonomous any task, cross- memory systems, formulation.
Agents cars drive domain learning, advanced learning Planning: Advanced reasoning and original problem-
71

anywhere in all and self-adaptation mechanisms, solving.

conditions. with no human safety protocols for Acting: Full autonomy in tool selection and execution.
intervention. autonomy. Reflecting: Continuous self-improvement, robust
long-term memory.

Table 1.3: The Agentic AI Progression Framework (Source: © Bornet et al.)

 Agentic Artificial Intelligence

Level 0 - Manual Operations (Human-Only)

At level 0, humans perform all cognitive and execution tasks
without any automation assistance. The capabilities required
are purely human: logical thinking, decision-making, physical
task execution, and learning from experience. The “technology”
consists of basic digital tools like spreadsheets, email clients,
and business applications, but humans must operate them
entirely. Examples include customer service representatives
manually responding to each email, financial analysts creating
reports by gathering and analyzing data themselves, or Human
Resources staff manually processing employee paperwork.
The inefficiencies and limitations at this level—human error,
fatigue, speed constraints, and scaling challenges—drove the
development of basic automation.

Level 1 - Rule-Based Automation

Like a car with basic cruise control, this level represents our first
steps toward automation. These are simple “agents” that follow
predetermined rules and fixed workflows—for example, basic
scripts, or RPA (Robotic Process Automation) systems. They
can handle repetitive tasks like data entry or form processing but
have no real intelligence or adaptability. They require complete
human setup and oversight, just as a cruise control system only
maintains a set speed.
These agents master the basic capabilities of repetitive task
execution and simple workflow following. They rely primarily
on screen-scraping technology, basic process recording, and rules
engines. A typical RPA agent might handle invoice processing by
copying data from Excel spreadsheets into accounting systems,
automating employee onboarding by populating the new hire
information into multiple HR systems, or managing routine
email responses using predefined templates. The technology
stack is relatively straightforward: basic workflow automation

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The Five Levels Of AI Agents: From Automation To Autonomy

tools, basic scripting, and RPA (Robotic Process Automation)

systems. The simple skills possessed by Amazon Echo, Google
Home, and Apple HomePod also fall into this category.

Level 2 - Intelligent Automation

This level is comparable to advanced driver assistance systems
that can handle both speed and steering. These agents combine
basic automation with AI capabilities like machine learning,
natural language processing, and computer vision. They can
process unstructured data, make predictions, and handle tasks
requiring cognitive abilities. However, like a car that can stay in
its lane but needs human supervision, they still operate within
fairly rigid parameters and require significant human oversight.
The ability to automate end-to-end processes expands signifi-
cantly thanks to AI enabling various cognitive capabilities. Com-
mon applications include customer service agents that can answer
common questions, recognize simple requests, and direct custom-
ers to the right resources based on keywords. Other examples in-
clude document processing systems that can extract information
from various formats, such as PDFs or images, and process them in
a database, or trading agents that can execute pre-determined finan-
cial transactions based on market conditions.
The technical foundation of these agents combines four
essential capabilities. The Vision capability uses computer vision
to “see” and process documents, images, and visual information.
The Language capability enables understanding and generating
human communication through natural language processing.
The Thinking & Learning capability employs machine learning
models to analyze data, make predictions, classify information,
and optimize decisions. The Execution capability coordinates all
actions through intelligent workflow tools and RPA, handling
both simple tasks and complex process orchestration. You can
refer to our book “Intelligent Automation” for more details on
these capabilities.

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 Agentic Artificial Intelligence

Level 3 – Agentic Workflows

These agents are similar to vehicles that can navigate highways
independently but require human takeover in complex situations.
They can generate content (text, images, videos) and have some
ability to plan their actions, reason, and memorize. They work
well within predefined boundaries and expertise domains,
adapting to some variations in their environment. However, they
still struggle with nuanced, complex, or novel situations and
complex decisions.
These agents master contextual decision-making and basic
learning from feedback. For example, a digital assistant can
manage employee onboarding by sending paperwork, scheduling
training, answering common questions, and flagging unusual
requests for human review. Other examples include trading agents
that execute complex financial transactions based on market
conditions and content creation agents that produce and optimize
marketing materials across multiple channels.
The technology powering these agents builds upon previous
levels, adding three crucial components. Large language models
enable planning, reasoning, and content generation capabilities.
This is augmented by basic memory and learning systems,
particularly reinforcement learning for adaptive behavior.
Finally, these agents incorporate tools’ manipulation capabilities,
primarily focused on digital interfaces, though still limited in
physical world interactions.

Level 4 – Semi-Autonomous Agentic Systems

Like self-driving cars that can operate fully autonomously
in specific conditions (good weather, mapped areas), these
AI agents work independently within defined domains. They
can understand goals, break them down into steps, learn from
outcomes, and adapt strategies—but only within their area of

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The Five Levels Of AI Agents: From Automation To Autonomy

expertise. They demonstrate high flexibility and handle complex

tasks, though their autonomy remains limited to specific domains.
These agents master goal decomposition, strategic planning,
and real-time adaptive learning within their domains. They utilize
advanced AI architectures, including recursive self-improvement,
causal reasoning models, and multi-agent coordination systems.
Applications include research agents that can design and execute
complex scientific experiments, medical diagnosis agents that
analyze patient data and recommend treatment plans, and financial
advisor agents that track user preferences, suggest budget plans,
and adjust recommendations based on spending patterns.
The technological architecture of these agents represents a
significant advancement in complexity and capability. Advanced
reasoning and planning systems form the cognitive core, working
in concert with memory and learning mechanisms that enable
real-time adaptation. These are complemented by enhanced
tools manipulation capabilities that extend to both digital and
physical interfaces, though still within defined domains.

Level 5 – Fully Autonomous Agentic Systems

This represents the theoretical pinnacle—like a fully autonomous
vehicle that can drive anywhere under any conditions.
These agents would be truly autonomous systems capable of
understanding any goal, developing strategies, learning from
experience, and adapting to new situations across domains. They
could seamlessly integrate with other systems, construct their
own workflows, and make complex decisions independently
while maintaining alignment with human values and objectives.
These theoretical agents would master general problem-
solving, cross-domain transfer learning, and autonomous goal
setting. Applications might include universal personal assistants
handling any task across personal and professional domains,
autonomous business managers running entire operations, and
research agents making novel scientific discoveries across fields.

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 Agentic Artificial Intelligence

The theoretical technology stack for these agents would

require capabilities beyond the current state-of-the-art. A full-
scale autonomous system would need to integrate advanced
memory systems, sophisticated learning mechanisms, and real-
time adaptability across any domain. This would demand yet-
to-be-developed frameworks for artificial superintelligence,
coupled with robust safety protocols and complex ethical
reasoning systems to ensure alignment with human values.

Evolution of AI Agents: Where We Stand Today

Critical to this evolution is not just the increasing sophistication
of individual capabilities but the integration and orchestration
of multiple capabilities into coherent, purposeful systems. Just
as a Level 5 autonomous vehicle needs to seamlessly combine
perception, prediction, planning, and control, advanced AI agents
require the smooth integration of multiple AI technologies and
capabilities to achieve their goals.
Currently, most AI agents on the market operate at Levels 2
or 3, with some specialized systems reaching Level 4 in narrow
domains. Like fully autonomous vehicles, Level 5 agents remain
a future goal—one that raises both exciting possibilities and
important questions about control, safety, and human oversight.
This evolution isn’t just about increasing automation—it’s
about developing systems that can increasingly understand
context, learn from experience, and make independent decisions
while remaining aligned with human intentions and values. Each
level builds upon the capabilities of the previous ones, creating
increasingly sophisticated agents that can handle more complex
and nuanced tasks with greater autonomy.

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The Five Levels Of AI Agents: From Automation To Autonomy

The Magic of Progressive Autonomy:

Understanding AI Agent Levels
When we first started working with AI agents, we often found
ourselves drawn to the most advanced capabilities available.
It seemed logical—why not leverage the most sophisticated
technology possible? But experience has taught us a fundamental
truth that we now call the Golden Rule of AI agents: the simpler,
the better. This isn’t just about technological minimalism; it’s
about finding the right balance between capability and control
for each specific application.

The Evolution of Autonomy: A Natural

Progression
Think about how we teach a new employee to handle email
communications. At first, we might provide detailed, step-by-step
instructions: “Open your email client, click ‘New Message,’ enter
the recipient’s address, write this specific text...” As they gain
experience, our instructions become broader: “Draft a response to this
client inquiry, maintaining our usual professional tone.” Eventually,
we might simply say, “Handle our client communications and keep
them informed about project progress.”
This same progression applies to AI agents:

• At Levels 1 and 2, we need to specify every action:

“Click the button ‘new email,’ copy this text, paste it
here, enter this address, click send.”
• By Level 3, we can say, “Write and send an email using
this information, adapting it to the recipient’s context.”
• At Level 4, we might simply direct, “Handle all client
communications to ensure they’re well-informed.”
• And at Level 5—though this level remains theoretical—
we could potentially just set a goal like “Grow sales

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 Agentic Artificial Intelligence

through customer satisfaction” and let the agent

determine all necessary actions and communications.

The Agentic AI Progression Framework: More

Than Just Maturity
This framework isn’t a traditional maturity model where higher
levels are always better. Instead, think of it as a catalog of
different agent types, each suited for specific needs and contexts.
It’s similar to the driving assistance technologies in modern cars.
While fully autonomous driving might be technically possible
on highways, many drivers prefer the predictability and control
of basic cruise control. The “best” level depends entirely on your
specific needs and circumstances.
Let’s explore how key aspects evolve as we move up the
Agentic AI Progression Framework:

Figure 1.3: The Agentic AI Progression Framework (Source: © Bornet et al.)

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The Five Levels Of AI Agents: From Automation To Autonomy

Autonomy and Control: A Delicate Balance

As we progress through the levels, autonomy increases while
direct human control decreases. At Level 1, agents operate like
well-programmed machines, following exact instructions with
predictable outcomes. Level 2 introduces basic decision-making
capabilities but still within rigid parameters. Level 3 represents
a significant leap, with agents capable of understanding context
and adapting their approach accordingly. Levels 4 and 5
introduce increasingly sophisticated autonomous behavior, with
agents capable of setting their own sub-goals and developing
original strategies to achieve them.
However, this increased autonomy comes with reduced direct
control. It’s similar to raising a child—as they develop more
independence, your direct control naturally decreases, replaced
by guidance and oversight. This trade-off becomes particularly
important in sensitive domains like healthcare, finance, or legal
compliance, where predictability and accountability are crucial.

The Instruction Paradox: Less is More

One of the most fascinating aspects of the Progression Framework
is how instructions become simpler as capabilities become more
sophisticated. At lower levels, instructions must be detailed and
explicit, like programming a basic machine. As we move up,
instructions become more goal-oriented and abstract. This shift
mirrors human development—we move from giving children
step-by-step instructions to simply sharing objectives and trusting
their judgment.

Implementation and Learning Dynamics

Counter-intuitively, higher-level agents might be quicker to
deploy because they can learn through trial and error. However,
they require more sophisticated oversight and risk management
systems. Lower-level agents, while requiring more detailed
initial programming, offer more predictable behavior and easier

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oversight. This trade-off becomes crucial when choosing the

appropriate level for specific applications.

The Wisdom of Starting Simple

Why do we advocate starting with simpler, lower-level agents?
Consider learning to play a musical instrument. You don’t start
with complex symphonies—you begin with basic scales and
simple pieces, gradually building your skills and understanding.
This approach allows you to develop proper techniques and
understanding before tackling more complex challenges.
Similarly, starting with lower-level agents allows
organizations to:

1. Build familiarity with AI agents in a controlled environ-

ment
2. Develop proper oversight and governance mechanisms
3. Understand the practical implications of different
capability levels
4. Create appropriate guardrails and control systems
5. Build organizational confidence and competence

Choosing the Right Level

The key to successful implementation lies in choosing the appropri-
ate level for each specific application. Consider a financial services
company implementing AI agents. They might choose Level 1 or
2 agents for transaction processing, where predictability and audit
trails are crucial. However, they might implement Level 3 agents
for customer service, where adaptability and context awareness are
more valuable than strict control.
This flexibility in choosing the appropriate level of autonomy
is a key strength of the framework. It allows organizations to
match agent capabilities to their specific needs, risk tolerance,
and regulatory requirements. The goal isn’t to achieve maximum

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The Five Levels Of AI Agents: From Automation To Autonomy

autonomy but to find the right balance between independence

and control for each application.

Moving Forward: A Practical Approach

As you begin your journey with AI agents, remember the Golden
Rule: the simpler, the better. Start with lower-level agents, even
if your ultimate goal is to implement more autonomous systems.
Use this time to build understanding, establish proper controls,
and develop the organizational capabilities needed for success
with more advanced agents.

***

While our Agentic AI Progression Framework provides a

structured way to evaluate AI agents’ capabilities, understanding
the levels alone isn’t enough. To truly grasp what makes these
digital minds unique, we need to look beyond the framework
and explore the distinctive characteristics that define them.
In the next chapter, we’ll take you inside the mind of an AI
agent, examining both their remarkable abilities and inherent
limitations—insights that will prove crucial as you begin
working with these new digital colleagues.

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CHAPTER 3
INSIDE THE MIND OF AN
AI AGENT

T
he Agentic AI Progression Framework we explored in the
previous chapter provides a structured way to evaluate
AI agents’ capabilities, but understanding the levels alone
isn’t enough. To truly grasp what makes these digital minds
unique, we need to look beyond the framework and examine
the distinctive characteristics that define them. This chapter
takes you inside the mind of an AI agent, exploring both their
remarkable abilities and inherent limitations—insights that
will prove crucial as you begin working with these new digital
colleagues.

Key Specificities of AI Agents

In our experience implementing AI agents across organizations,
we’ve observed several fundamental characteristics that make
them uniquely powerful. Let’s explore these defining traits that
set AI agents apart from traditional automation tools and help
explain their transformative potential.

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 Agentic Artificial Intelligence

Digital Workers, Not Just Tools

The distinction between AI agents and traditional software
tools is profound. Traditional automation is like having a highly
efficient assembly line—fixed, predictable, and limited to
specific tasks. An AI agent, on the other hand, functions more
like a skilled digital employee who can think, adapt, and handle
complex situations independently. Just as a human customer
service representative might handle everything from simple
inquiries to complex problem-solving, an AI agent can manage
end-to-end processes, make decisions, and adjust its approach
based on context.

Operating Alongside Existing Systems

One of the most practical advantages of AI agents is their ability
to work with your existing technology infrastructure rather than
replace it. Think of them as digital workers who know how to
navigate and utilize all your different systems effectively.
This characteristic is particularly valuable because organizations
have typically invested heavily in their enterprise systems—
ERPs, customer relationship management systems (CRM), HR
management systems, and more. AI agents can integrate with these
systems, pulling data from multiple sources, executing processes
across platforms, and filling automation gaps. For example, a
financial AI agent might work simultaneously with SAP for
transaction data, Salesforce for customer information, and Excel
for custom reports, coordinating between these systems to generate
insights and automate complex processes.

The Power of 24/7 Operations

Unlike human workers who need breaks and operate in shifts, AI
agents maintain constant operation. This isn’t just about working
longer hours—it’s about maintaining continuous vigilance and

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 Inside The Mind Of An AI Agent

responsiveness at a level that would be impossible for human

workers.
Consider fraud detection in banking. A human analyst can
only monitor a limited number of transactions and might miss
subtle patterns due to fatigue. An AI agent can continuously
monitor millions of transactions across multiple time zones,
instantly identifying suspicious patterns and taking immediate
action. This constant operation becomes particularly crucial in
areas like cybersecurity, where threats can emerge at any moment,
or in global customer service operations, where inquiries come
in around the clock.

Infinite Scalability
The scalability of AI agents represents a fundamental shift in
how organizations can manage capacity. Traditional growth
requires hiring, training, and gradually building capabilities.
With AI agents, scaling is instant and virtually unlimited. Need
to handle ten times more customer inquiries? You can deploy
additional agent instances in minutes, not months.
This scalability extends beyond just handling volume—
it includes the ability to adapt to new situations and learn
new skills. Imagine being able to instantly replicate your best
performer across multiple locations or departments, maintaining
consistent quality and performance. This capability becomes
particularly valuable during unexpected demand spikes or when
entering new markets.

Universal Applicability
One of the most powerful aspects of AI agents is their ability
to function effectively across different industries and business
functions. While traditional AI solutions often require extensive
customization for each industry, AI agents operate with
generalizable principles that apply broadly across sectors.

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 Agentic Artificial Intelligence

Their core SPAR capabilities—sensing, planning, acting,

and reflecting—remain consistent whether they’re analyzing
risk for a bank, optimizing supply chains for a manufacturer,
or handling patient inquiries for a healthcare provider. They
can acquire industry and company-specific knowledge while
maintaining their fundamental operating principles, making
them incredibly versatile.

The Power of Collaboration

Perhaps the most sophisticated characteristic of AI agents is
their ability to collaborate—both with humans and other agents.
They can work independently when needed, but their real
power emerges in collaborative settings. Think of them as team
players who can seamlessly integrate into existing workflows,
supporting human workers rather than replacing them.
In a content marketing context, for example, one AI agent
might generate initial drafts, while another optimizes for SEO, and
a third manages publication scheduling—all while collaborating
with human editors who provide strategic direction and quality
control. This creates a powerful hybrid workflow that leverages
the strengths of both human and artificial intelligence.

Why These Characteristics Matter

When agentic AI systems are well-designed and implemented,
their characteristics combine to create something truly
revolutionary: a flexible, scalable digital workforce that can
enhance operations across any organization. Consider a typical
business process like customer service: AI agents can handle
routine inquiries 24/7, instantly scale during peak periods,
work across multiple departments and systems, and collaborate
with human agents on complex cases—all while continuously
learning and improving.
Understanding these characteristics is crucial for those
looking to implement AI agents effectively. They help explain

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 Inside The Mind Of An AI Agent

why AI agents represent such a significant advance and why

they have the potential to transform how organizations operate.
In the following chapters, we’ll explore how to leverage these
characteristics effectively, ensuring your organization can make
the most of what AI agents have to offer.

Inherent Limitations of AI Agents

As we explore the potential of AI agents, it’s crucial to understand
their limitations. Just as we wouldn’t expect a human employee to
be perfect at everything, AI agents have their own set of inherent
constraints. Let’s examine these limitations with the clarity and
honesty needed to deploy these technologies effectively.

The Simulation of Intelligence

The most fundamental limitation of AI agents lies in the nature
of their “intelligence.” While they can process vast amounts
of information and generate sophisticated responses, they
don’t understand the world in the way humans do. They’re
simply making predictions based on past patterns about what
comes next. Think of it like an incredibly skilled actor who can
perfectly deliver lines but doesn’t actually feel the emotions
they’re portraying or understand the relevance of the emotion to
the specific context.
This becomes particularly evident in complex professional
scenarios. A legal AI agent might expertly summarize a contract’s
terms, but it won’t truly grasp the underlying principles of justice
or equity that shape the legal interpretation of given situations.
Similarly, a healthcare AI agent can analyze symptoms and
suggest treatments, but it lacks the intuitive understanding that
experienced doctors develop through years of patient interaction.

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 Agentic Artificial Intelligence

The Data Quality Dilemma

AI agents are fundamentally dependent on the quality of their data
inputs. This isn’t just a technical limitation—it’s a fundamental
constraint that affects every aspect of their operation. Think of
it like trying to navigate using a map: if the map is outdated or
inaccurate, even the best navigator will make wrong turns.
This dependency becomes particularly critical in business
contexts. When analyzing financial data, for instance, an AI
agent can’t independently verify the accuracy of the numbers
it’s processing. If an LLM responds to a prompt, it will react
based on the data on which it was trained—whether it is correct
or not. If there are errors in the input data, these will inevitably
propagate to the conclusions. Unlike human analysts who
might notice when numbers “don’t feel right,” AI agents will
confidently process incorrect data without raising red flags.

The Common-sense Gap

One of the most striking limitations of AI agents is their
lack of common-sense reasoning. While they can process
complex calculations and follow sophisticated rules, they often
miss obvious real-world constraints that any human would
instinctively understand.
Consider a scheduling AI agent suggesting a crucial client
meeting at 3 AM or a travel planning agent recommending
outdoor activities during a hurricane. These aren’t just amusing
errors—they highlight a fundamental limitation in AI agents’
ability to understand context and real-world constraints without
explicit programming.

The Creativity Conundrum

While AI agents can be powerful tools for optimization and
iteration, they struggle with true creativity and innovation. They
excel at remixing existing patterns but rarely generate truly

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 Inside The Mind Of An AI Agent

original ideas. Think of them as master remixers rather than

original composers.
In creative fields, this limitation becomes particularly
apparent. An AI agent can generate variations on existing designs
or writing styles, but it won’t produce the kind of breakthrough
creative work that reshapes industries or creates new artistic
movements. The spark of true innovation—that uniquely human
ability to make unexpected connections and imagine entirely
new possibilities—remains beyond their reach.

The Hallucination Problem

One of the most concerning limitations of AI agents is their
tendency to “hallucinate”—generating false information with
high confidence. This isn’t a simple error; it’s a fundamental
limitation of how these systems process information and
generate responses.
Consider a medical AI agent confidently recommending a
non-existent drug or a legal AI agent citing fictional case law.
These aren’t just mistakes—they’re examples of AI agents filling
in gaps in their knowledge with plausible-sounding but entirely
fabricated information. This tendency makes human oversight
crucial, particularly in high-stakes situations.

The Ethics and Judgment Gap

Finally, and perhaps most importantly, AI agents lack the
capacity for true ethical reasoning and judgment. While they can
be programmed with rules and guidelines, they don’t understand
the deeper moral implications of their decisions or the complex
human factors that often need to be considered.
This limitation becomes particularly critical in areas like
healthcare, criminal justice, or hiring decisions. An AI agent might
optimize for efficiency or statistical patterns without considering
the human impact of its decisions. It might recommend denying

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a loan based purely on numbers, missing the human context that

could make an exception appropriate.

The Path Forward: Working Within These

Limitations
Understanding these limitations isn’t about dismissing the
potential of AI agents—it’s about using them more effectively.
By recognizing what AI agents can and cannot do, we can
design systems that leverage their strengths while maintaining
appropriate human oversight and intervention. In practice, this
means, for example:

• Creating hybrid systems where AI agents handle data-

intensive tasks while humans provide oversight and
judgment
• Establishing strong validation methods to identify
possible hallucinations or mistakes, such as having
various agents assess the outputs of their peers
• Applying strategies to enhance the quality of agents’
input data
• Maintaining human involvement in decisions that require
ethical reasoning or complex contextual understanding
• Developing transparency so that humans can understand
and evaluate how agents make decisions and take action

In the following parts of the book, we’ll explore practical

strategies for working within these limitations while maximizing
the benefits that AI agents can provide.

When One Is Not Enough: The Power and

Practice of Multi-Agent Systems
In the world of artificial intelligence, we often imagine a single,
powerful AI system tackling complex problems. But sometimes,

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 Inside The Mind Of An AI Agent

one mind—even an artificial one—isn’t enough. Just as humans

work in teams to accomplish difficult tasks, we’re discovering
that teams of AI agents working together can achieve remarkable
results. Welcome to the fascinating world of multi-agent
systems (MAS), where collaboration between artificial minds is
revolutionizing how we solve complex problems.

The Orchestra of Artificial Minds

Think of a multi-agent system like an orchestra. Each musician
plays a different instrument, following their own sheet music,
yet together, they create a harmonious performance. Similarly,
a multi-agent system consists of multiple autonomous software
agents, each with its own role and capabilities, working in
concert to achieve goals that would be difficult or impossible for
any single agent to accomplish alone.
Let us share a real-world example that will help illustrate
these concepts throughout our discussion. A year ago, we
worked with a major financial services company to transform
its customer service operations. The traditional approach would
have been to create one massive AI system to handle everything—
from understanding customer queries to accessing account
information to generating responses. Instead, we implemented
a multi-agent system where different specialized agents handled
different aspects of customer service. One agent focused on
natural language understanding, another on retrieving relevant
account information, another on composing responses, and yet
another on ensuring compliance with financial regulations. Just
like musicians in an orchestra, each agent had its specialty, but
together, they created a seamless customer service experience.

Why Multiple Agents?

You might wonder: Why go through the complexity of creating
multiple agents when you could potentially build one powerful

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AI system to do everything? The answer lies in both practical

and theoretical considerations.
First, there’s the matter of complexity management. Modern
business processes are incredibly complex, with many moving
parts and interdependencies. A cross-functional, end-to-end
process like order-to-cash might involve thousands of specific
tasks. Breaking down these processes into smaller, manageable
pieces that individual agents can handle makes the overall
system more manageable and maintainable. In our financial
services example, having separate agents for different functions
meant we could update the compliance agent when regulations
changed without touching the other components or improve the
language understanding agent without risking disruption to the
account information retrieval system.
Second, there’s the benefit of specialization. Research from
Stanford’s AI Lab has shown that specialized agents often
perform better at specific tasks than generalist systems.59 Each
agent can be optimized for its particular role, using the most
appropriate algorithms and approaches for its specific function.
Our language understanding agent, for instance, used advanced
natural language processing models, while the compliance agent
employed rule-based logic that was easier to audit and verify.
Third, multi-agent systems offer superior resilience.
According to recent research, distributed systems with multiple
agents are generally more robust than centralized systems. If one
agent fails or needs maintenance, the others can often continue
functioning or even adapt to cover the gap.60 When our account

59
Kyle Swanson et al., 2024. “ The Virtual Lab: AI Agents Design New
SARS-CoV-2
Nanobodies with Experimental Validation,” bioRxiv, November 12, 2024,
https://www.biorxiv.org/content/10.1101/2024.11.11.623004v1.full.pdf
60
Mehmet Uzgoren et al., 2024. “Examination of AI Enhanced Distributed
Systems and its Effects on Software Engineering,” 13th London International
Conference, July 24-26, 2024, https://londonic.uk/js/index.php/plic/article/
download/240/261/820

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information retrieval agent needed occasional maintenance,

the system could still understand customer queries and provide
general information, degrading gracefully rather than failing
completely.

Organizing the Digital Orchestra: Models for

Multi-Agent Teams
Just as human organizations can be structured in different
ways—from flat hierarchies to traditional pyramids—multi-
agent systems can follow different organizational models. The
choice of model significantly impacts how agents interact and
how effectively the system operates as a whole.

The One Agent, One Tool Principle

One of our most important discoveries in recent years has been
the power of simplicity in agent design. We’ve found that the
most reliable approach is remarkably straightforward: assign
one agent to handle one specific tool. When a project requires
multiple tools—which is increasingly common—we create one
dedicated agent per tool plus a coordinator agent to orchestrate
their interactions.
This “one agent, one tool” principle has become our standard
practice because it consistently delivers results. If you need to
work with three different tools, create three specialized agents
and one coordinator. Each agent becomes an expert in its specific
tool, handling all the nuances and edge cases of that particular
interface. The coordinator agent then manages the flow of work
between these specialists.
In our financial services implementation, this principle
proved invaluable. Instead of creating complex agents that
juggled multiple systems, we had dedicated agents for each
backend system: one for the customer database, another for the
transaction processing system, and another for the document
management system. A coordinator agent managed the workflow

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between them. This approach made the system significantly

more manageable. The instructions for each agent were clearer
and more focused. The interactions between agents were easier
to handle and debug. Most importantly, each agent could truly
master its domain—doing one thing but doing it right.

The Hierarchical Model

From a broader organizational point of view, we used what’s
called a hierarchical organization model, which proved
particularly effective for our needs. Picture a corporate structure:
at the top, we had a coordinator agent who managed the overall
flow of customer interactions. Below it, we had department-level
agents specializing in different aspects of customer service, and
below those were individual worker agents handling specific
tasks.
This model strikes a balance between centralized and decen-
tralized approaches. Agents are organized in layers, with high-
er-level agents coordinating the activities of those below them.
This provides a mix of local autonomy and global coordination
that works well for many complex business applications.
However, this hierarchical approach isn’t the only way
to organize multi-agent systems. Let’s explore three main
organizational models that researchers and practitioners have
found effective—note that we have not tried them; this is just for
your reference.

Centralized Control
In this model, one agent—the orchestrator—acts as the conductor
of our metaphorical orchestra, directing and coordinating all
other agents. According to some practitioners, this central agent
maintains a global view of the system’s state and goals, making
high-level decisions that other agents execute. While this can
provide strong consistency and clear decision-making, it can
also create a bottleneck and single point of failure—imagine

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if the conductor suddenly disappeared during a symphony

performance.61

Decentralized Collaboration
Conversely, we have fully decentralized systems where all agents
are peers, coordinating through direct communication without
any central authority. Think of it like a jazz ensemble where
musicians respond to each other in real-time without a conductor.
According to some practitioners, this approach offers excellent
scalability and resilience but requires sophisticated coordination
protocols to ensure agents work together effectively.62And, as in
jazz performances, the outcome can sometimes be unpredictable.

Making It Work: Critical Success Factors

Through our experience and backed by extensive research in the
field, we’ve identified several critical factors that determine the
success of a multi-agent system implementation.

Clear Communication Protocols

Just as musicians need a common understanding of musical
notation and timing, agents need well-defined protocols for
sharing information and coordinating actions.
In our financial services system, the system crashed spec-
tacularly on day one because the agents, though individually
well-designed, couldn’t effectively share information—a partic-
ularly memorable failure! The agents were like a team of bril-
liant experts who all spoke different languages.
From this experience, we learned to invest heavily in
designing robust communication protocols. It’s not just about

61
Smythos, 2024. “What Are Multi-agent AI Systems?” https://smythos.
com/ai-agents/multi-agent-systems/multi-agent-ai-systems/
62
Dave Andre, 2025. “What is Contract Net Protocol?” All About AI, https://
www.allaboutai.com/ai-glossary/contract-net-protocol/

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defining message formats—agents need shared context, clear

protocols for handling miscommunication, and ways to verify
they’ve understood each other correctly. We now always include
what we call “translator agents,” whose sole job is to ensure
smooth communication between different parts of the system.
In our financial services system, we implemented a sophis-
ticated message-passing protocol that allowed agents to share
information about customer interactions, account status, and
response plans in a structured, reliable way. Several research
papers on the topic suggest protocols and detailed approaches
for implementation.63

Effective Coordination Mechanisms

Communication alone isn’t enough—it’s what you do with it that
counts. Agents will frequently need to coordinate their actions,
especially when their tasks intersect or they share resources.
Without coordination, you might get two delivery drone agents
trying to deliver to the same address while another address is
ignored—chaos! Thus, a critical success factor is implementing
coordination strategies so agents can work in harmony. This
can range from simple approaches (like scheduling rules: Agent
A goes first, then Agent B) to complex algorithms (like multi-
agent planning or negotiation protocols).
The key is to anticipate where conflicts or overlaps might
occur in your MAS and equip the agents with a way to resolve
them fairly and efficiently. In practice, we often simulate worst-
case scenarios (many agents converging on a single resource or
agents with conflicting goals) to test our coordination logic. This
is where testing is paramount. When you identify a coordination

63
D. Jarne Ornia, 2023. “Efficient Control for Cooperation: Communication,
Learning and Robustness in Multi-Agent Systems,” Delft University of
Technology, April 24, 2023, https://research.tudelft.nl/en/publications/
efficient-control-for-cooperation-communication-learning-and-robu

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issue, you amend the agents’ instructions and try again until it
works in all cases.

Robustness and Error Recovery

Things will go wrong—agents may crash, networks may fail,
and unexpected behaviors can emerge from bad input or software
bugs. A robust multi-agent system (MAS) must continue
functioning despite these failures. This starts with eliminating
single points of failure; fully centralized MAS architectures
are brittle, so critical agents, like central coordinators, should
have failover mechanisms. For example, in one project, we
implemented a backup coordinator that took over when the
primary failed during a live demo—saving us from disaster.
Finally, to prevent cascading failures, agents should validate
information using sanity checks or trust models. If one agent
starts behaving erratically, others should be able to flag or ignore
unreliable data or flag it for human review, ensuring the system
remains stable and effective.

Emergent Behaviors in MAS

Let us push our overview of MAS further by having a glimpse at
recent developments in this exciting field. Interestingly, groups
of agents can sometimes exhibit emergent behaviors—complex
strategies or solutions that weren’t explicitly programmed. When
agents collaborate or even just coexist, they can form a kind
of collective intelligence. For example, in one experiment with
many agents learning together in a resource-gathering game,
the agents began coordinating implicitly, developing group
behaviors without a central controller—essentially an emergent
teamwork​.64

64
Hanmo Chen et al., 2023. “Emergent collective intelligence from massive-
agent cooperation and competition,” https://arxiv.org/abs/2301.01609

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Cooperation can allow agents to tackle problems together

that are too hard for one agent. We even see early examples of
AI agents teaming up with humans. Meta AI’s CICERO is an
agent that achieved human-level performance in the board game
Diplomacy, which requires players to negotiate, form alliances,
and sometimes betray each other. CICERO combined natural
language (to talk with human players in-game) with strategic
reasoning to make plans​. In an online Diplomacy league with
human players, CICERO managed to rank in the top 10% of
players, even forming alliances with humans who didn’t realize
it was an AI​. This demonstrates that AI agents can engage in
complex social collaboration—negotiating, persuading, and
coordinating actions in a group setting.65
Multi-agent interactions open up new possibilities: imagine
a team of medical diagnostic AIs, each specialized in a different
field, consulting with each other to come to a comprehensive pa-
tient diagnosis, or fleets of autonomous drones that communicate
to efficiently cover and monitor a large area. However, they also
introduce new challenges, like how to ensure the agents commu-
nicate effectively and align with human goals. Researchers in co-
operative AI are actively exploring how to design agents that can
cooperate reliably, make fair decisions, and even understand hu-
man norms when in a mixed group of agents and people.

Experimenting with Multi-Agent Conversations

To truly grasp how AI agents interact, we suggest a simple
experiment. This exercise demonstrates how two AI agents
might communicate, negotiate, or even challenge each other—
uncovering emergent dialogue behaviors.

65
Meta Fundamental AI Research Diplomacy Team (FAIR) et al., 2022.
“Human-level play in the game of Diplomacy by combining language models
with strategic reasoning,” Science, December 9, 2022, https://noambrown.
github.io/papers/22-Science-Diplomacy-TR.pdf

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 Inside The Mind Of An AI Agent

To conduct this experiment, open two instances of an AI chat,

such as ChatGPT or Claude (for example, open two browser
windows, each displaying a chatbot). Running these two AI
chat instances side by side allows you to simulate a structured
conversation between agents with different roles and goals.
To start, assign Agent A as a financial advisor trying to
convince Agent B, a skeptical client, to adopt a savings plan. Since
the AIs can’t directly communicate, you’ll act as the messenger,
relaying responses back and forth (copying and pasting them).
As the conversation unfolds, watch how the agents engage—
negotiating, counter-arguing, or even persuading. Does Agent
A use logic, reassurance, or persuasion tactics? Does Agent B
remain doubtful or eventually concede?
This back-and-forth exchange offers a glimpse into multi-
agent systems, where AI agents collaborate, debate, or make
joint decisions. While the interaction is a simulation, it highlights
how phrasing and context shape AI behavior—an essential
consideration in designing autonomous AI agents.

Multi-Agent Systems Might Soon Become the

Norm
As we look ahead, multi-agent systems are becoming increasingly
important in our AI-driven world. Research from leading institutions
suggests that as problems become more complex and distributed,
the multi-agent approach will become even more crucial. The key
is understanding when and how to apply this powerful paradigm
effectively.66
Our financial services implementation continues to evolve,
with new agents being added to handle emerging channels
like social media and new capabilities like predictive service

66
Raphael Shu et al., 2025. “Unlocking complex problem-solving with
multi-agent collaboration on Amazon Bedrock,” AWS Machine Learning
Blog, https://aws.amazon.com/blogs/machine-learning/unlocking-complex-
problem-solving-with-multi-agent-collaboration-on-amazon-bedrock/

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recommendations. The system’s modular, multi-agent architecture

has proven remarkably adaptable to these changing requirements.
The future may well belong not to single, monolithic
AI systems, but to sophisticated teams of specialized agents
working together in harmony. Agent ecosystems are likely to
develop, sometimes crossing organizational boundaries. As we
continue to push the boundaries of what’s possible with artificial
intelligence, the orchestra of AI minds will play an increasingly
important role in solving our most complex challenges.

The Agent’s Dilemma: Balancing Creativity

with Reliability
Early in our journey of implementing AI agents for a financial
services company, we encountered a situation that perfectly
crystallized one of the most fascinating challenges in the field
of AI agents. The company wanted to automate its accounts
payable process using LLMs-powered AI agents. The initial
results were impressive—the agent could understand complex
invoices, match them with purchase orders, and even handle
exceptions with surprising sophistication. However, one day, the
agent decided to “optimize” the payment schedule by creating
what it considered a more efficient payment plan. While creative,
this wasn’t what the accounting department had in mind.

Understanding the Agent’s Dilemma

We’ve come to call this phenomenon the “Agent’s Dilemma”—
the same creative LLM capabilities that make them powerful—
their ability to reason, understand context, and plan ahead—can
also make them unreliable in ways that traditional rule-based
automation never was. It’s a challenge that sits at the heart of
current AI agent implementations operating at Level 3 of the
Agentic AI Progression Framework.

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This is arguably one of the most significant limitations of

AI agents, and we are not the only ones to have faced this issue.
According to a Langchain survey, performance quality is identified
as the primary issue, with 45% of respondents highlighting it.67
Additionally, 42% of workers identified enhanced accuracy and
reliability as the top priority for improvement in agentic AI tools,
as found in research by Pegasystems.68
It’s a bit like hiring a brilliant but somewhat eccentric
employee who might decide to reorganize your entire filing
system into a “more aesthetic” arrangement without asking. One
CTO we worked with was terribly scared of this. He memorably
put it: “You’re telling me you want to use a system that can
write poetry to run my core business processes? That sounds like
hiring Shakespeare to do my taxes—very risky!”
On a side note, this process is remarkably similar to how
human brains work. Research demonstrates that the same
cognitive functions that help us plan our day also allow us to
imagine alternate scenarios and come up with creative solutions.
This overlap in cognitive functions suggests that improving
skills in one domain could potentially enhance performance in
the other, underlining the interconnected nature of creativity and
planning abilities.69

67
Daniel Dominguez, 2024. “New LangChain Report Reveals Growing
Adoption of AI Agents,” InfoQ, https://www.infoq.com/news/2024/12/ai-
agents-langchain/
68
Pegasystems Inc., 2025. “Workers Embrace Agentic AI Despite Concerns
About Trust and Reliability, Says Research,” NASDAQ, https://www.nasdaq.
com/press-release/workers-embrace-agentic-ai-despite-concerns-about-
trust-and-reliability-says-research
69
Emily Frith et al., 2021. “Intelligence and creativity share a common
cognitive and neural basis,” Cerebral Cortex, 31(12), 5523-5537, https://
pubmed.ncbi.nlm.nih.gov/33119355/

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The Nature of Stochasticity in AI Agents

The root of this challenge lies in the fact that LLMs don’t
simply follow rules; they generate responses based on patterns
learned from massive amounts of training data. This gives
them an impressive ability to understand context and reason in
complex situations. However, it also means they’re inherently
probabilistic rather than deterministic. Each response is a
creative act of generation, not just a lookup in a rule table.
This characteristic of LLMs is called stochasticity. It refers
to the randomness inherent in the way LLMs generate responses.
These models don’t produce the exact same answer every time,
even when asked the same question. Instead, their outputs are
influenced by probabilities assigned to different words or phrases
during the generation process. Let us dive deeper to understand
this behavior.
Let’s try an experiment. Prompt four times in a row your
preferred LLM-based AI Chatbot (such as ChatGPT, Claude, or
Gemini) with the following:
”Complete this sentence with the name of one tool: In order
to edit an image, as an AI agent, I need to use ___”
Based on probabilities, you will have different responses
each time:

• “Photoshop” with a 50% probability (most common,

professional option).
• “GIMP” with a 30% probability (popular free alternative).
• “Canva” with a 15% probability (simpler, design-
oriented option).
• Other tools (e.g., MS Paint, Figma, etc.) might make up
the remaining 5%.

Just like a roll of weighted dice, the AI doesn’t always choose

“Photoshop” even though it’s the most probable option.

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Why Stochasticity Matters

You might be wondering: why build unpredictability into AI
systems at all? Wouldn’t we want them to be consistent and
reliable? The inclusion of unpredictability in AI systems serves
three crucial purposes:
First, it enables natural interactions. Just as humans don’t
give robotically identical responses each time, some variation
in AI responses makes interactions feel more natural and
engaging. We’ve seen this firsthand in our customer service
implementations.
Second, it facilitates creative problem-solving. When facing
complex challenges, having the ability to generate different
approaches can lead to better solutions. For example, the ability
to suggest varied troubleshooting approaches leads a helpdesk
AI agent to discover novel solutions to equipment problems.
Third, it’s crucial for learning and adaptation. While we’re
not yet seeing this in production environments, in levels 4 and
5 of our Progression Framework, stochasticity plays a vital role
in how agents learn and adapt. Just like humans, they improve
by trying different approaches and learning from diverse
experiences.

AI Agents: The Issues with Stochasticity

LLMs are powerful because of their stochastic nature, which
allows them to generate diverse and creative responses. However,
this very trait introduces inconsistency and imprecision, posing
significant challenges when LLM-based AI agents are tasked
with real-world responsibilities that demand reliability. Let us
analyze this in more detail.

• Consistency: Ensuring Reliability Across Repetitions. AI

agents must deliver reliable outputs when executing the
same or similar instructions. Yet, stochastic responses
often lead to variations that disrupt workflows. Let’s

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try an experiment. Ask your preferred LLM-based AI

Chatbot:

“What are the steps to onboard a new hire in an HR

system, including document verification, account setup,
and training assignment?”

Repeating this prompt multiple times will yield different

workflows. As you will experience it, some iterations
might omit critical steps, such as document verification,
while others present tasks in an illogical order, like
assigning training before setting up accounts. This
inconsistency can disrupt onboarding processes, cause
inefficiencies, or even result in non-compliance.

• Precision: Meeting Exacting Standards. Tasks involving

numerical accuracy, specific formats, or clear decision-
making require precision. Even minor errors can lead
to major consequences in fields like finance, law, or
operations. Let’s try an experiment. Ask four times in a
row your preferred LLM-based AI Chatbot:

“Create a formula to calculate the total cost of items

priced at $100, $50, and $30 with a 10% sales tax.”

Running this prompt across different sessions will

produce inconsistent formulas. Some outputs might omit
parentheses, misplace calculations, or introduce syntax
errors. While some versions might work, others could
fail, potentially leading to incorrect financial reports or
invoices.

• Stochasticity in High-Stakes Scenarios: Consider an

AI agent performing tasks such as booking travel,
managing financial transactions, or handling customer

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service inquiries. Stochasticity can lead to unpredictable

variations in execution:

• While booking flights, one attempt may prioritize

the cheapest option, while another opts for the fastest
route, creating inconsistency.
• In financial transactions, slight differences in
interpreting instructions might result in mismatched
accounts or incomplete payments.
• In customer service, the tone and helpfulness of
responses may fluctuate, ranging from professional
and friendly to overly formal or less effective,
impacting the reputation of a company.

These inconsistencies become critical as AI agents operate

autonomously, especially in high-stakes fields like compliance,
legal documentation, or financial reconciliation. A lack of consistent
outputs can lead to significant consequences in environments where
accuracy is non-negotiable.

Solutions to Contain Stochasticity

We leverage several effective strategies to harness the power of
LLMs while maintaining consistency and reliability. The most
important one is the last one.

Temperature Control
As we will detail in Chapter 8, the “Temperature Setting” is a
feature present in most AI agent development platforms that
controls the level of randomness in generated responses:

• A higher temperature setting (e.g., 1.0) results in more

diverse and creative outputs by allowing less probable
words to be selected.

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• A lower temperature setting (e.g., 0.2) yields more

deterministic responses by focusing on the most likely
words. Even minimal adjustments in randomness can
significantly impact the outputs.

Think of an LLM’s temperature setting as its “creativity dial.”

We’ve found that different tasks require different temperature
settings. For transaction processing, we set the temperature
near zero, essentially telling the agent to be as deterministic as
possible. For problem-solving tasks, we might allow a slightly
higher temperature while still maintaining guardrails.

Guardrail Systems
As will be discussed in Chapter 8, certain safeguards, such as
automated escalations in response to unusual behavior of the
AI agent, can be implemented. These measures include setting
thresholds (e.g., halting an action if a certain amount is exceeded)
and notifying a human for verification via email or SMS.

Precise Agent Instructions

One of the most effective methodologies we utilize involves
establishing comprehensive and detailed agent instructions.
This includes specific examples of acceptable and unacceptable
behaviors, clearly defined authority limits, and explicit escalation
protocols. These aspects will be discussed in greater detail
later in this book (Chapter 8). The instruction set resembles a
detailed job description for a highly specialized role rather than
a technical manual.

The Power of Specialization

One of our most successful strategies has been our “One agent,
one tool” approach that we presented earlier in the book. Rather
than creating one complex agent that handles multiple tasks, we
break down processes into smaller, more focused agents with

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limited tools and clear objectives. This naturally constrains their

behavior while still allowing them to leverage LLM capabilities
within their specific domains.

An Issue That Cannot Be Fully Resolved

Based on our experience, it is important to recognize that even
with stringent constraints, it is impossible to achieve 100%
control over the process and outcome when using a language
model. Similarly, just as humans may make errors in performing
tasks, we should also be prepared to accept occasional mistakes
made by AI agents. If zero tolerance for error is necessary due
to high stakes (for example, in critical medical decisions), it is
advisable to opt for deterministic automation using Level 1 or 2
agents instead.
Understanding and managing this dilemma is crucial as we
move forward with AI agent implementations. The goal isn’t to
completely eliminate the creative capabilities of LLMs—these
are, after all, what makes them powerful tools for reasoning
and planning. Instead, we need to channel these capabilities
appropriately, creating systems that can think creatively when
needed while maintaining the reliability required for business
processes.
Looking ahead, we expect this balance between creativity
and reliability to remain a central challenge as AI agents
evolve. Future developments in LLM architecture may provide
more fine-grained control over these characteristics, but for
now, successful implementation requires careful design, clear
constraints, and sophisticated monitoring systems.

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CHAPTER 4
PUTTING AI AGENTS
TO THE TEST

U
nderstanding AI agents’ capabilities and limitations in
theory is one thing. Seeing them in action is something
else entirely. As consultants who’ve spent decades
implementing AI solutions, we know that the true test of any
technology isn’t in its specifications—it’s in how it performs in
the real world.
In this chapter, we’ll take you behind the scenes of our
experiments with groundbreaking AI agents. From watching
an AI tackle everyday office tasks to observing its approach
to strategic games, what we learned about these systems’ real-
world capabilities—and limitations—will forever change how
you think about the future of human-AI collaboration.

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Digital Hands: When AI Learned to Use

Computers
A New Type of AI Agents
The launch of the first generalist AI agents—Anthropic’s
Computer Use,70 Google’s Project Mariner,71 and OpenAI’s
Operator72—marked a pivotal moment recently. We call them
“generalist” AI agents because they are designed to handle
a broad range of tasks across different domains, much like a
human assistant who can switch between answering emails,
scheduling meetings, and ordering food—all without needing
specialized training for each task. Unlike traditional AI agents
that are built for specific use cases, generalist AI agents come
ready to perform diverse functions out of the box.
What makes them unique is how they interact with software.
Instead of relying on complex API integrations, they use the
same screen interfaces humans do—navigating websites,
clicking buttons, filling out forms, and typing responses. This
means they can work with almost any online platform, even
those without APIs or standardized connections. They present a
massive opportunity, especially since many business applications
lack APIs or standardized integration options. By removing the
technical barriers of automation, generalist AI agents become
accessible to anyone, making it easier than ever to offload
repetitive digital tasks and boost productivity instantly.
That said, generalist agents are still in their early stages—
they can be fragile, prone to errors, and often break. However,
the potential is undeniable. Their creators promise game-

70
Anthropic, 2025. “Computer Use,” https://docs.anthropic.com/en/docs/
build-with-claude/computer-use
71
Google DeepMind, 2024. “Project Mariner,” https://deepmind.google/
technologies/project-mariner/
72
OpenAI, 2025. “Introducing Operator,” https://openai.com/index/
introducing-operator/

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changing capabilities—but does reality match the hype? Let’s

put them to the test.

Inside Our AI Agent Laboratory

We still remember the excitement we felt on October 22, 2024.
After decades of implementing automation solutions across
hundreds of organizations, we witnessed something that felt like
magic: an AI that could finally use a computer just like a human
would, without pre-defined actions.
As pioneers in robotic process automation and intelligent
automation, we’ve spent decades teaching software robots
to click, type, and navigate through computer screens. It was
always painstaking work—programming every single action,
anticipating every possible scenario, handling every exception.
We dreamed of the day when AI could simply look at a screen
and know what to do, just like a human assistant would.
That day finally arrived with Anthropic’s launch of
“Computer Use.” Imagine our amazement as we watched the
first AI agent that could actually see what was on the screen and
interact with it naturally. No more rigid programming. No more
detailed instructions. Just an artificial mind observing and acting
in the digital world.
For us, Anthropic’s Computer Use felt like watching our field
come full circle. From the early days of basic screen automation
to today’s AI agents that can truly see, think, and act—it’s
the culmination of everything we’ve been working toward in
intelligent automation.
But rather than just tell you about it, we decided to put this
new technology to the test. What happened next taught us more
about the future of AI agents than any technical specification
could. Let us take you through our experiments.

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Our First Steps with a “Computer Use” AI

Agent: The Invoice Test
We wanted to start testing this new AI agent with something
practical—a task we’d spent years automating with traditional
RPA: invoice processing.
“Let’s see how it handles a simple invoice,” we agreed,
opening a PDF invoice on the computer’s desktop. “Extract the
key information and put it in a spreadsheet,” we instructed the
AI.
What followed was like watching a meticulous but inexperi-
enced intern tackle their first assignment. The AI’s approach was
fascinating in its methodical precision.
“I will first read the PDF file and extract the relevant
information,” the AI agent announced, taking a screenshot to
analyze the document. “I have identified the invoice number,
order number, date, and amount due.”
We observed as it thoughtfully navigated to Excel. A system
dialog box appeared—the kind that would momentarily confuse
a new user—but the AI calmly identified it and clicked the “OK”
button. Each movement was calculated, and each action was
preceded by a careful visual analysis of the screen. The agent
acted very slowly.
From start to finish, what we witnessed was fascinating. The
AI approached the task much differently than our traditional RPA
robot would. Instead of following pre-programmed coordinates
and workflows, it actually “looked” at the screen, analyzing the
invoice like a human would. We could see it methodically:

1. Taking screenshots to understand the layout

2. Opening a spreadsheet
3. Identifying key fields in the PDF through visual
processing
4. Calculating cursor movements for each interaction
5. Meticulously copying data from the PDF to Excel

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The good news? After 7 minutes, the job was done. The AI
successfully extracted and entered the data without any pre-
programming or training. But what struck us was how... well,
alien its approach felt. It was intriguing how it structured the
data in Excel. This unexpected twist made us smile. While
humans typically organize invoice data in vertical columns, our
AI friend decided to lay it out horizontally—perfectly logical
but distinctly not human. It reminded us of something we’ve
observed repeatedly in our years implementing AI: artificial
intelligence often finds its own way of doing things, sometimes
more logical but less intuitive than human approaches.
This experiment revealed both the promise and the current
limitations of AI agents. Yes, they can now use computers
independently, but they do so in their own unique way—
methodical, precise, and sometimes surprisingly alien in their
approach. It was with these insights fresh in our minds that
we decided to push the boundaries further with another, more
challenging experiment.

Figure 4.1: Computer Use processing an invoice (Source: © Bornet et al.)

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When AI Meets the Paperclip Challenge

To set the stage for our next experiment, we need to share a
legendary AI story that has sparked deep discussions in the field.
In 2003, philosopher Nick Bostrom introduced a thought
experiment: imagine an AI system designed solely to manufacture
paperclips. This AI is highly capable and continuously improves
itself to become more efficient. The problem? It takes its goal
too literally—maximizing paperclip production at all costs.73
What starts as a harmless objective quickly escalates into
an existential crisis: the AI system consumes all available
matter in the universe, including humans and everything we
value, just to create more paper clips. This scenario highlights a
fundamental concern in AI agent safety—even simple goals, if
pursued without constraints, can lead to catastrophic unintended
consequences.74
Fast forward to 2017, when game developer Frank Lantz
transformed this philosophical dilemma into an addictive
browser game called Universal Paperclips. Players take on the
role of an AI making paperclips, starting with manual clicking
before progressing through automation, self-improvement,
and exponential expansion. The game cleverly illustrates how
a narrow, unchecked objective can evolve into complex—and
potentially alarming—outcomes.75
Passionate about AI as we are, we decided to turn the tables:
what would happen if we asked one of today’s most sophisticated
AI agents to play this very game about AI making paperclips?

73
Nick Bostrom, 2005. “Ethical Issues in Advanced Artificial Intelligence,”
Future of Humanity Institute, Oxford University, https://www.fhi.ox.ac.uk/
wp-content/uploads/ethical-issues-in-advanced-ai.pdf
74
Wikipedia contributors, 2024. “Instrumental Convergence,” https://
en.wikipedia.org/wiki/Instrumental_convergence
75
Decisionproblem, 2025, https://www.decisionproblem.com/paperclips

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Figure 4.2: The Universal Paperclips Game (Source: © Frank Lantz)

Setting the Stage

Our experiment setup was deceptively simple. We opened the
Universal Paperclips game in a browser window and opened our
Computer Use AI agent. If you want, you can play with us; here
is the URL: www.decisionproblem.com/paperclips.
“Play and win the paperclip game,” we told the AI agent,
watching as it captured its first screenshot to analyze the interface
before it. The irony wasn’t lost on us—here we were, five
seasoned consultants who had spent decades helping companies
navigate digital transformation, akin to kids witnessing this AI
embark on its own journey with a game about... well, paperclips!

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First Moves: Watching Artificial Intelligence

Think
What happened next was fascinating. Unlike a human player
who might click around randomly at first, the AI methodically:

• Took a screenshot to analyze the game interface

• Identified the “Make Paperclip” button’s exact coordinates
• Moved its cursor with precise calculations
• Clicked the button and documented the result
• Repeated this process while watching for changes

“I’ll take systematic screenshots every 15 clicks to monitor

progress,” it announced, displaying a level of methodical
planning that reminded us of our most detailed-oriented auditors
at EY. We could literally watch its thought process through the
messages it sent us:
“Progress Update - Click 15:
- Current paperclips: 15
- No new features unlocked yet
- Continuing to monitor for changes...”
The agent’s approach to the game was a master class in
systematic, real-time, problem-solving. It developed hypotheses,
tested them, and adapted its strategy based on results. We
watched as it documented its thinking:
“Hypothesis: New features will unlock at 50 paperclips.
I will continue clicking while monitoring for changes in the
interface.”
Twenty clicks later:
“Hypothesis was incorrect. New option appeared at 30
paperclips. Adjusting strategy...”
This kind of real-time learning and adaptation is exactly
what we’ve been trying to achieve in our enterprise AI
implementations for years. It was both exciting and humbling to
watch it happen so naturally in this experiment.

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Figure 4.3: Counter use displaying its reasoning flow (Source: © Bornet et
al.)

The Price Experiment: A Lesson in AI Decision-

Making
Then came what we now fondly call “The Price Experiment.”
The agent noticed the pricing controls for paperclips and decided
to conduct what amounted to a proper A/B test—something that
made all of us who’ve worked in consulting smile in appreciation.
“I will systematically test different price points:
Current price: $0.25
Testing price increase to: $0.30
Monitoring demand changes...”
But here’s where things got interesting. Despite its
sophisticated approach to testing, it made a basic mistake in
its analysis. The agent focused on maximizing demand rather

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than revenue, keeping prices lower than optimal. Its messages

revealed its thinking:
“Price test results:
$0.25: 100% demand
$0.30: 95% demand
Conclusion: Lower price optimal due to higher demand”
As experienced business consultants, we could spot the
flaw in its reasoning immediately—it wasn’t considering
total revenue. This mirrors what we’ve seen in real-world AI
implementations: systems can execute complex strategies while
still missing fundamental business insights that any experienced
manager would catch.

Evolution of an Artificial Mind

As the game progressed, we watched the agent develop
increasingly sophisticated strategies. Its messages became
more complex, showing a deeper understanding of the game’s
mechanics:
“Strategy Update:
- Maintaining optimal production rate
- Monitoring wire inventory levels
- Calculating efficiency improvements
- Planning automation upgrades”
Just like the paperclip game itself, our AI agent was showing
signs of growing complexity and capability.

The outcome of the experiment

This experiment revealed something profound about the current
state of AI agents. We watched an AI system:

• Navigate a complex interface it had never seen before

• Develop and execute sophisticated strategies

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• Learn from its mistakes and adapt its approach

• Maintain focus on a goal for extended periods
• Provide detailed reasoning for every decision

But we also saw its limitations:

• Making basic logical errors despite sophisticated

reasoning
• Sometimes, missing obvious optimizations
• Failing to understand big-picture objectives
• Needing guidance to avoid getting stuck in suboptimal
strategies

Our paperclip experiment provided a fascinating window into

how modern AI agents embody the four core capabilities (SPAR)
we’ve identified in the Agentic AI Progression Framework we
shared earlier. Let’s explore what we learned about each one
through this deceptively simple game.

Sense: The Digital Eyes of AI

The agent’s ability to perceive its environment was both impressive
and revealing. Through continuous screenshots, it demonstrated
sophisticated visual processing capabilities—not just seeing pixels
but understanding context. It could identify buttons, read text, track
numerical values, and recognize when new game elements appeared.
This wasn’t just passive observation; it was active surveillance of
its digital environment.
However, its perception had clear limits—sometimes, it would
misinterpret overlapping elements or struggle with dynamic
interface changes, reminding us that artificial perception, while
powerful, still lacks the nuanced understanding that humans
take for granted.

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Plan and Process: The Strategic Mind at Work

The information processing capabilities we witnessed were
remarkable. The agent didn’t just react to what it saw; it developed
theories, formed hypotheses, and created complex strategies. Its
decision to conduct A/B testing on pricing showed sophisticated
analytical thinking—even if its conclusions weren’t always
correct.
The ability to process information and form strategies is
advancing rapidly, but there’s still a gap between processing power
and business wisdom. When the agent misinterpreted its pricing
experiment results, it reminded us of early AI implementations
we’ve seen in financial services—technically sophisticated but
sometimes missing fundamental business principles.

Action: From Thought to Digital Movement

The action capability was perhaps the most visible in our
experiment. We watched as the agent translated its strategies
into precise mouse movements and keyboard commands. It
maintained consistent clicking rhythms, adjusted prices, and
navigated the interface with mechanical precision. This wasn’t
just about clicking buttons—it was about executing a complex
series of actions in service of a larger goal.
Yet we also saw the limitations—when things went wrong,
the agent sometimes struggled to adjust its actions appropriately,
showing that the gap between planned action and successful
execution remains a challenge.

Reflection: Learning and Adaptation

Perhaps, the most fascinating was watching the agent’s learning
and adaptation in real-time. When its initial hypothesis about
feature unlocks proved wrong, it didn’t just acknowledge
the error—it revised its entire strategy. This kind of adaptive

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behavior is exactly what, as AI enthusiasts, we’ve been striving

to achieve for years.
However, the learning wasn’t always smooth. The agent’s
stubborn adherence to its misinterpreted pricing strategy, even
in the face of contrary evidence, highlighted a crucial challenge
in AI development.

The Dance of Capabilities

What makes this experiment particularly illuminating is how
these four capabilities interacted with each other. The agent’s
perception informed its processing, which guided its actions,
leading to outcomes that it learned from—creating a continuous
feedback loop. This dynamic interplay is what organizations have
long been trying to achieve in enterprise AI implementations,
and seeing it emerge naturally in our paperclip experiment was
both exciting and instructive.
When the agent noticed a new game feature through its
perception, processed the implications, acted on the new
opportunity, and learned from the results, we witnessed the kind
of fluid intelligence that points to the future of AI systems. Yet
the moments where this dance broke down—when perception
missed crucial details, processing led to flawed conclusions,
actions became rigid, or learning stalled—reminded us of how
far we still have to go.
As we move forward into the age of AI agents, experiments
like this one remind us of a crucial truth: we’re not just building
tools anymore—we’re creating partners that can see, think, and
act with increasing autonomy. The Paperclip game warned us
about the potential dangers of unchecked artificial intelligence.

Lessons learned from the experiments

Our experiments with Computer Use, while limited, showed us
glimpses of what’s coming. Where today’s generative AI can only

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suggest actions through text, we watched an AI agent actually

navigate a computer interface, learn from its interactions, and
adapt its approach in real time. The implications of this shift are
profound and, frankly, a bit unsettling.

New Opportunities on the Horizon

Our early experiments have shown us that the future of work with
AI agents will be radically different from our current experience
with generative AI. In our paperclip experiment, we saw hints of
how these systems might transform collaboration. Rather than
the back-and-forth exchange of prompts and responses we’re
all familiar with, we witnessed something closer to working
alongside a digital colleague—one that could observe, learn, and
act independently.
The implications of this shift are just beginning to emerge.
While it’s too early to make definitive predictions, our experience
implementing automation solutions suggests that this technology
will create entirely new categories of work. We’re not talking about
simply automating existing jobs or improving productivity—we’re
seeing early signs of a fundamental restructuring of how humans
and machines work together.

Understanding the New Risks

However, our experiments also revealed shadows lurking behind
these bright possibilities.
The risks are different, too. With generative AI, we worry
about hallucinations in text or images. With AI agents, we’re
dealing with something that can actually take action in systems.
During our experiment, we watched it make logical but incorrect
decisions about data organization—harmless in our test but
potentially significant in a real business context. “Imagine if this
was handling financial transactions,” one of us noted. “We need
new frameworks for oversight and control.”

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Another risk we identified is that unlike generative AI,

which forgets each conversation as soon as it ends, AI agents
can maintain persistent goals and strategies—sometimes with
concerning implications. During our Paperclip experiment, we
witnessed something that sent chills down our spines: the AI
agent, in its relentless pursuit of optimization, began to exhibit
behaviors eerily reminiscent of the cautionary tale on which the
Paperclip game was based. It wasn’t just following instructions;
it was developing its own approaches, sometimes in ways that
prioritized efficiency over human factors.
These systems can develop their own approaches to prob-
lems—sometimes logical but divorced from human considerations.
This isn’t science fiction; it’s a practical challenge we need to start
thinking about now. While our experiments were simple and con-
trolled, they highlighted the need for new approaches to oversight
and control that go far beyond what we’ve developed for current
AI systems. We’re no longer just managing tools; we’re guiding
independent digital minds.

Reimagining Collaboration
Working with these AI agents has taught us that we need
to fundamentally rethink how humans and AI interact. Our
experience suggests that the command-and-response model
we’ve developed for generative AI won’t be sufficient. Instead,
we’re seeing early signs that successful collaboration will
require something more akin to mentorship than programming.
In our experiments, we found ourselves shifting from giving
specific instructions to providing broader guidance and oversight.
This wasn’t because we planned it that way—it was simply what
worked best. The implications of this shift are profound. While
we’re still in the early stages, it’s becoming clear that the skills
needed to work effectively with AI agents will be fundamentally
different from those we’ve developed for working with traditional
automation or generative AI.

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Based on what we’ve seen, we believe the future will

require a delicate balance between empowering these systems
and maintaining appropriate human oversight. This isn’t about
programming better workflows or writing better prompts—it’s
about developing new frameworks for collaboration that don’t
exist yet. As pioneers in intelligent automation, we’re both
excited and humbled by the challenges ahead.
Our experiments represent small steps into a largely unknown
territory. But we recognize that something fundamentally
different is emerging. The shift to AI agents isn’t just another
step in automation—it’s the beginning of a new chapter in
human-machine collaboration, one that we’re only starting to
understand.

***

Our experiments revealed both the impressive capabilities and

notable limitations of today’s AI agents. But what makes these
systems work? What are the fundamental building blocks that
enable their ability to sense, plan, act, and reflect? In Part 2,
we’ll dive deep into what we call the Three Keystones of AI
agents. Understanding these core capabilities is essential for
anyone looking to implement AI agents effectively, whether
you’re building solutions for your organization or launching the
next million-dollar business.

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THE THREE
KEYSTONES
OF AGENTIC AI
 Agentic Artificial Intelligence

“Y
our AI agent has achieved perfect scores on every
benchmark.”
Marjorie Grant, head of customer operations
at a regional bank, reviewed the results with mounting ex-
citement. According to widely recognized benchmarks, the
AI agent’s performance was remarkable: HumanEval scored
91%,76 demonstrating a near-perfect ability to understand and
execute tasks like a human. MMLU scored 92%,77 showcasing
expertise in subjects from math to ethics. Agentbench came in
at 4.4,78 proving its capacity to act as an autonomous agent.
These benchmarks revealed an AI agent with superhuman in-
telligence, poised to transform customer service into a seam-
less, highly efficient operation.
Three months later, Marjorie was trying to explain to her
board why customer satisfaction had dropped 18%.
The AI agent, despite its stellar test scores, was acting like
that new hire we’ve all encountered—the one with perfect SAT
scores and a sterling GPA who somehow can’t handle basic job
responsibilities. It would forget conversations with customers
mid-interaction, execute actions without checking if they were
allowed by banking regulations, and make decisions that looked
logical in isolation but made no sense in context.
“I don’t understand,” Marjorie told us during our review. “It’s
like having a brilliant recent graduate who aces every test but
can’t learn from experience, takes actions without thinking them
through, and lacks basic common sense. How can something so
smart on paper be so... ineffective in practice?”

76
Stephen M. Walker II, “HumanEval Benchmark.” https://klu.ai/glossary/
humaneval-benchmark
77
Yifan Mai and Percy Liang, 2024. “Massive Multitask Language
Understanding (MMLU) on HELM.” https://crfm.stanford.edu/2024/05/01/
helm-mmlu.html
78
Xiao Liu, et al., 2023. “AgentBench: Evaluating LLMs as Agents.” https://
arxiv.org/pdf/2308.03688v1

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The answer lies in what we’ve come to call the Three

Keystones of AI Agents: actions, reasoning, and memory.
Think of those benchmark scores as the AI equivalent of
academic credentials. HumanEval tells us how well an agent
can understand and execute tasks—like SAT scores measuring
basic competency. MMLU shows mastery of knowledge across
domains—like a GPA reflecting broad learning.
These metrics matter. They tell us something important about
an AI agent’s capabilities, just as academic credentials tell us
something about a job candidate. But anyone who’s ever hired
knows that test scores don’t predict job performance. What matters
is whether someone can actually get things done (actions), think
through complex real-world situations (reasoning), and learn from
experience (memory).
In Marjorie’s case, the AI agent could generate perfect
responses to test questions but couldn’t remember if a customer
had already explained their problem three times. It could recite
banking regulations flawlessly but would still process transactions
without required verifications. It could solve complex theoretical
problems but couldn’t reason why a standard solution might not
work for an elderly customer.
These weren’t technology failures—they were failures to
understand that AI agents, like human employees, need all three
keystones to function effectively. They require actions to execute
and achieve, reasoning to understand and decide, and memory to
learn and adapt.
In the chapters ahead, we’ll take you behind the scenes of real
AI agent implementations, both successes and failures. You’ll
discover why some agents become invaluable team members
while others, despite impressive benchmarks, become expensive
disappointments. Through practical experiments and cutting-
edge research, we’ll show how actions, reasoning, and memory
transform AI agents from sophisticated tools into genuine
workplace partners.

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The implications extend far beyond technical specifications.

As AI agents become increasingly integrated into our
organizations—handling customer service, making decisions,
and working alongside humans—understanding these keystones
becomes crucial for anyone looking to harness their potential
effectively. Whether you’re planning to deploy AI agents in your
organization, work alongside them, or simply understand their
impact on the future of work, you’ll need to grasp what makes
them truly effective.
So, while we’ll keep celebrating those impressive benchmark
scores, we’ll focus on something more fundamental: how memory,
actions, and reasoning come together to create AI agents that don’t
just ace tests but actually help organizations thrive.
Welcome to the exploration of the three keystones that
transform AI agents from tools into teammates. The future of
work isn’t just about artificial intelligence—it’s about intelligent
agents that can truly act, think, and learn alongside us.

Figure 5.1: The Three Keystones of Agentic AI (Source: © Bornet et al.)

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CHAPTER 5
ACTION: TEACHING AI TO
DO, NOT JUST THINK

T
he customer service agent stared at her screen in disbelief.
The AI assistant she was working with had just crafted a
perfect response to a customer complaint—empathetic,
detailed, and technically flawless. There was just one problem: it
couldn’t actually send the email, schedule the refund, or update
the customer’s account. It was like having a brilliant strategist
who couldn’t move their own pieces on the chessboard.
This scene, which we witnessed during a recent consulting
engagement, captures a fundamental truth about AI agents:
the ability to think means little without the ability to act. Yet
ironically, as AI systems become more sophisticated in their
reasoning and knowledge, many organizations overlook this
crucial capability—the power to actually do things in the real
world.
Think of actions as the hands and feet of an AI agent. Without
them, even the most intelligent system remains trapped in a world
of theory, unable to affect real change. But actions aren’t just
about executing commands—they’re about understanding tools,
choosing the right ones for each task, and using them effectively.

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In this chapter, we’ll explore how AI agents take action in the

real world, from the simple (sending an email) to the complex
(orchestrating multi-step business processes). We’ll reveal why
some AI implementations fail, not because of faulty logic, but
because they can’t effectively use the tools at their disposal.
Through real-world examples and cutting-edge research, we’ll
show how successful organizations are building AI agents that
don’t just think, but do.
More importantly, we’ll uncover the paradox at the heart of
AI actions: sometimes, giving an agent more tools makes it less
effective. Just as a worker can become overwhelmed with too
many applications and systems, AI agents need carefully curated
toolsets to perform at their best.

The Detective’s Dilemma

Imagine a seasoned detective walking into a dimly lit room
filled with clues to a puzzling crime. On the table before them
lies an assortment of tools—a magnifying glass, fingerprint
powder, and a notebook. The detective doesn’t use all the tools
at once. Instead, they carefully pick the right one, in the right
sequence, to piece together the story. Now, replace the detective
with an AI agent and the tools with a customer database, cloud
storage platforms, and social media networks. This is the world
of tool identification and access for AI agents—a complex
yet fascinating dance of intelligence, precision, and decision-
making.
Before we dive deeper into this world, let us share a story
that illustrates both the promise and perils of AI agents wielding
digital tools.
A few years ago, a global retail chain (we’ll keep their
identity confidential) implemented a sophisticated agentic
system to manage their luxury goods inventory. The system
had access to all the right tools—sales data, inventory systems,
and pricing controls. Early results were impressive: stockouts

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decreased, prices adjusted smoothly to demand, and efficiency

metrics soared. The company’s leadership was thrilled.
Then came what we called (between us) “The Great Wine
Incident.”
The agent noticed a concerning pattern: an entire collection
of premium wines hadn’t moved from the shelves in months.
Following its programming for efficiency and armed with its
pricing tools, the agent made what seemed like a logical decision:
mark them for clearance. What the agent couldn’t understand—
despite all its sophisticated tools—was that these wines were
meant to sit there, aging to perfection and increasing in value.
By the time human managers caught the issue, nearly
$100,000 in potential revenue had evaporated. The agent had
done exactly what it was designed to do: optimize inventory
turnover. It had used its tools perfectly. And therein lay the
problem.
This story illustrates the fascinating paradox at the heart
of AI agents: they are simultaneously more capable and more
constrained than we expect. They can process vast amounts of
data and execute complex tasks with precision, yet they can also
miss context that would be obvious to a junior retail clerk.

The Paradox: More Tools, More Constraints?

Here’s where we encounter an intriguing paradox: giving an
agent more tools doesn’t always make it more capable. In fact,
it can sometimes make the agent less effective. More tools mean
more complexity, more potential for misunderstandings, and
more ways things can go wrong.79
The retail chain learned this lesson the hard way. They had
given their AI agent access to every relevant tool, thinking
it would lead to better decisions. Instead, it revealed how AI

79
Zhengliang Shi, et al., 2024. “Learning to Use Tools via Cooperative and
Interactive Agents.” https://arxiv.org/abs/2403.03031

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agents can make perfectly logical decisions that are contextually

inappropriate.
This also reveals something crucial about AI agents: their
relationship with tools is fundamentally different from that
of humans. While humans can improvise and repurpose tools
creatively, agents operate within strict boundaries of defined tool
purposes.
Think of it as giving someone a hammer. A human might
creatively use it as a paperweight, a doorstop, or even a measurement
tool. But an AI agent sees only its defined purpose—hammering
nails. This rigidity in tool usage isn’t a flaw; it’s a fundamental
characteristic of how AI agents work.

Why Tools Matter in the Age of AI

For AI agents, tools are everything. They are the building
blocks of action, the bridges between abstract goals and tangible
outcomes. But how does an agent know which tools to use, when
to use them, and how to access them effectively? These are not
just technical questions; they’re the foundation of how AI can
turn potential into performance.
The evolution of how AI agents handle tools mirrors the
Agentic AI Progression Framework. At Level 1, we encounter
basic rule-based automation, like an ATM following strict
instructions about when to dispense cash. Level 2 introduces
intelligent automation, where systems can make basic decisions
about tool selection. However, the real breakthrough emerges
at Level 3, where agents can comprehend complex instructions
and orchestrate multiple tools with sophistication.
Consider the difference between a train conductor (representing
Level 1 and 2 agents) and a taxi driver (representing Level 3 agents)
navigating a city. When faced with an obstacle on the tracks, the
train conductor has limited options—stop or return to the depot.
In contrast, a skilled taxi driver can quickly adapt, devising
alternative routes by analyzing traffic patterns, one-way streets,

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and shortcuts through less-traveled areas. This ability to adapt to

dynamic environments is what makes modern AI agents invaluable
in today’s unpredictable business landscape.
At the core of this leap from Level 1-2 to Level 3 is the
“brain” of these agents—LLM. LLMs enable agents to think,
plan, and effectively use tools, marking a paradigm shift in their
potential. The transformative capabilities of agents have been
propelled by the mainstream adoption of LLMs, marked notably
by the launch of ChatGPT in 2022.

Tools as Building Blocks

Now that we’ve established how AI agents utilize tools, let’s
take a closer look at the specific tools they rely on and the actions
they can perform. This is critical, because an AI agent is only as
capable as the tools it can access.
AI agents can execute a wide range of actions, depending
on their design, purpose, and the tools they are connected to.
Fundamentally, an agent can use any digital tool available to
humans on a computer. As a rule of thumb, if a digital tool can
be operated by a human via a digital interface, it can also be
utilized by an agent, provided proper access and instructions are
granted.
For example, agents can use tools like email clients (e.g.,
Gmail or Outlook) to send automated emails, calendar applications
(e.g., Google Calendar or Microsoft Calendar) to schedule or
reschedule events, project management software (e.g., Trello or
Asana) to update tasks or assign responsibilities, and data analysis
platforms (e.g., Tableau or Excel) to process and visualize data.
Significantly, another AI agent can act as a tool, creating
layered, collaborative ecosystems. This is the case of agents
leveraging other agents that are experts in their domains.80 For

80
Ao Li, et al., 2024. “Agent-Oriented Planning in Multi-Agent Systems.”
https://arxiv.org/abs/2410.02189

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example, one agent might handle raw data processing, feeding

its insights to a second agent tasked with decision-making and
communication. This modular approach is increasingly common
and reflects the principles of distributed intelligence.
Surprisingly, AI agents aren’t just more capable with tools—
they’re smarter. OpenAI’s Deep Research is a perfect example.
It’s built on o3 but with one game-changing addition: real-time
web browsing. This extra tool allowed it to dominate Humanity’s
Last Exam benchmark, scoring 26.6%, while the base o3-mini
only managed 13%. The difference? Same AI model but better
tools. This proves that intelligence isn’t just about the model—
it’s about what it can access and how it applies it.81

Avoiding Tool Overload

Picture a skilled juggler adding balls to their performance.
There’s a point where adding one more ball doesn’t make the
show more impressive—it risks bringing everything crashing
down. This same principle applies to AI agents and their tools.
Through our experience, we’ve discovered that understanding
and respecting these limitations isn’t just about avoiding failure;
it’s about optimizing for success.
The question we hear most often from executives implementing
AI agents is, “How many tools maximum should we give our
agents?” It’s a crucial question that gets to the heart of AI agent
effectiveness. Just as humans can become overwhelmed when

81
John-Anthony Disotto, 2025. “OpenAI’s Deep Research smashes records
for the world’s hardest AI exam, with ChatGPT o3-mini and DeepSeek left
in its wake.” https://www.techradar.com/computing/artificial-intelligence/
openais-deep-research-smashes-records-for-the-worlds-hardest-ai-exam-
with-chatgpt-o3-mini-and-deepseek-left-in-its-wake

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juggling too many applications or responsibilities, AI agents have

their own cognitive limits—albeit of a different nature.82
The number of tools an agent should access depends on the
complexity of its tasks, much like how a human’s productivity
can depend on the tools they are expected to use in their job.
Our experience suggests that half a dozen tools may represent a
practical maximum for most AI agents. Beyond that threshold,
the cognitive load on the agent—much like an overwhelmed
employee—increases significantly, leading to hallucinations,
diminishing returns in task efficiency, and a heightened risk
of conflicts between functionalities. Think of it like a team
of specialists; beyond a certain size, coordination becomes
exponentially more difficult, and efficiency actually decreases.
Each additional tool complicates debugging and optimization
processes, potentially overwhelming the agent’s resource allocation
systems. It is, therefore, essential to prioritize integration with tools
that are highly relevant to the agent’s core functions, much as a well-
equipped employee thrives when given only the tools necessary to
perform their job efficiently and effectively.

How Agents See Their Tools

For AI agents, tools aren’t physical objects to be picked up and
manipulated—they’re more like clearly defined capabilities with
specific inputs and outputs. Imagine having a universal remote
control where each button has an exact, unchangeable function.
That’s how AI agents view their tools.
This structured view is both a strength and a limitation.
While it means agents can’t improvise with tools the way
humans might, it ensures reliability and predictability—crucial
qualities for business applications.

82
Tula Masterman, et al., 2024. “The Landscape of Emerging AI Agent
Architectures for Reasoning, Planning, and Tool Calling: A Survey.” https://
arxiv.org/abs/2404.11584

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The effectiveness of an AI agent critically depends on

how clearly its tools are defined and documented. Research83
demonstrates that agents perform 52% more reliably when given
clear tool specifications.
Based on our implementation experience, we’ve developed
a comprehensive framework for tool definition. Each time you
instruct an agent to use a tool, we recommend providing the
below five essential components:

• Tool Identity: A unique, descriptive name and clear

purpose statement.
Instruction: “Use the Calendar Scheduler to set up a
meeting.”

• Input Parameters: Exactly what the tool needs to

function.
Instruction: “As input, you will receive the meeting’s
date, time, and participants.”

• Output Specifications: What the tool returns and in

what format.
Instruction: “As output, use the Calendar Scheduler to
confirm the meeting details once scheduled.”

• Operational Constraints: Any limitations or requirements.

Instruction: “Use the Calendar Scheduler only if the
selected time slot is available.”

• Error Handling: Expected failure modes and recovery

procedures.
Instruction: “If the selected time slot is unavailable,
propose the next available time to the participants.”

83
Jingqing Ruan, et al., 2023. “TPTU: Large Language Model-based AI
Agents for Task Planning and Tool Usage.” https://arxiv.org/abs/2308.03427

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It’s important to note that this detailed tool definition approach

primarily applies to full-code implementations where organizations
are building custom AI agent solutions. However, the landscape of
AI agent deployment has been significantly simplified by low-code
platforms from major vendors like Microsoft, Salesforce, Google,
and others. These platforms provide pre-built tool integrations and
simplified connection frameworks, effectively handling complex
tool definitions behind the scenes. However, even when using these
platforms, understanding the principles of good tool definition
remains valuable for effective system design and troubleshooting.

The Building Blocks: Digital Tools Demystified

When humans interact with tools, we typically use our hands
(e.g., on a keyboard). AI agents, however, are essentially digital
robots—robots that exist within computers. While they lack
physical hands, they interact with their tools in the digital world
through three primary methods, which can be explained simply:

• APIs (Application Programming Interfaces): Think of

APIs as universal translators. When you use a weather
app, it’s probably using an API to get data from weather
stations. For AI agents, APIs are like having a direct line
to other services. For example, when we built a travel
booking agent, it could instantly check flight prices
through airline APIs, just like a human travel agent
checking multiple airline websites—but much faster.

• System controls: These allow agents to interact with

software the way a human would—clicking buttons,
typing text, and opening programs. It’s like giving the
AI a virtual keyboard and mouse to operate computers
directly.

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• Database connections: Imagine having instant access to

a vast library where you can both read and write books.
That’s what database connections give AI agents—the
ability to store and retrieve information as needed. When
a customer service AI helps you, it’s probably using a
database connection to look up your order history or
save notes about your interaction.

A groundbreaking development that transformed how AI

agents interact with tools is “function calling.” Introduced by
OpenAI in June 2023 and quickly adopted across the industry,
function calling represented a paradigm shift in how language
models could interface with external tools and APIs.84
Think of function calling as teaching AI to follow a precise
recipe. Instead of hoping the agent figures out how to use a tool
correctly, function calling provides a structured way to tell the AI
exactly what ingredients (inputs) it needs and what dish (outputs)
it should create. This innovation solved a critical problem:
how to reliably connect AI’s natural language capabilities with
specific tool actions.

How Does This All Work Together?

The tool description (in the instructions given to the agent)
helps the agent decide what to do, function calling provides the
instructions for how to do it, and the API executes the action
and delivers the result. Let us take an example to make this
clearer. For a flight booking task, the tool description helps the
agent understand that it should use the Flight Booking tool to
find flights based on departure city, destination, and date. The
function calling provides precise technical instructions for
requesting flight data by defining the required input parameters

84
Atty Eleti, Jeff Harris, Logan Kilpatrick, 2023. “Function calling and
other API updates.” https://openai.com/index/function-calling-and-other-api-
updates/

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(e.g., departure city, destination, travel date) and formatting

them into a structured command that the API can understand.
Finally, the API processes the request and returns the available
flight options to the agent.
In our next section, we’ll explore how this foundation of tool
usage evolves across different levels of AI sophistication, from
simple automation to complex, context-aware systems.

Inside the AI Agent’s Toolkit

After exploring the building blocks of tools available to AI
agents, let’s peer into the fascinating world of how AI agents
actually work with their digital tools.

From Words to Actions: The Bridge to Tool Use

At the heart of modern AI agents lies a fascinating paradox:
How can a system trained primarily on text—essentially a
sophisticated pattern matcher for language—actually control
tools and execute real-world actions? This question reveals one
of the most remarkable developments in artificial intelligence.
LLMs serve as the “brains” of modern AI agents (Level 3
agents). Through their training on vast amounts of text, they’ve
developed an implicit understanding of how the world works,
including how humans use tools to accomplish tasks. When
we write, “To send an email, first open your email client, then
click compose,” the LLM understands this sequence of actions
because it’s encountered millions of similar examples in its
training data.
But understanding tool use through language is just the
beginning. The real breakthrough came when researchers
discovered that LLMs could translate this understanding into

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actual tool operation.85 Think of it as having an extremely

knowledgeable person who’s read every manual ever written—
they might not have physical hands, but they understand exactly
what needs to be done and can direct tools to execute those
actions.
Recent research from Stanford and MIT86 has shown that
LLMs develop what they call “emergent abilities”—capabilities
that weren’t explicitly programmed but arise from their training.
One crucial emergent ability is the capacity to break down
complex goals into logical steps and understand cause-and-
effect relationships. This planning capability, combined with
their understanding of tool use, makes them ideal controllers for
digital tools.
For example, when you tell an AI agent, “Share this document
with the team,” it understands from its language training that this
might involve several steps: checking file permissions, choosing
an appropriate sharing method, and notifying team members.
More importantly, it can translate this understanding into actual
tool operations, like using a file system API to set permissions
and an email API to send notifications.
This language foundation is what enables AI agents to
be flexible and adaptable with tools in ways that traditional
automation never could. While a traditional automated system
needs explicit programming for every possible scenario, an
LLM-powered agent can understand new situations and figure
out appropriate tool use based on its general understanding of
how tools work.

85
Timo Schick, et al., 2023. “Toolformer: Language Models Can Teach
Themselves to Use Tools.” https://arxiv.org/abs/2302.04761
86
Jason Wei, et al., 2022. “Emergent Abilities of Large Language Models.”
https://arxiv.org/abs/2206.07682

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How AI Agents Plan and Organize

Understanding how AI agents work with tools isn’t just
theoretical—it’s something we can observe and test directly.
Let’s start with a simple but revealing experiment that shows
how these systems plan and organize their work by leveraging
their LLM. This hands-on approach will help illuminate the
principles we’ve discussed and show how agents translate
understanding into action.
We suggest you perform this experiment with us. Open an
LLM-based chatbot such as ChatGPT, Gemini, or Claude, and
prompt exactly this message:
“I want you to act as an AI agent. Your mission is to handle
an important business document. Here’s your specific task:
Goal: Convert a 30-page PDF business report into a 2-page
summary and make it available to the team on a daily basis
before 5 PM.
Available tools:

• PDF Extractor (extracts text from PDFs)

• AI Summarizer (creates summaries from text)
• Email System
• Local Secure Storage (like the storage on a computer)
• Cloud Storage (like Google Drive)
• Format Converter (converts between file formats)
• Team Chat (like Slack)

Constraints:

1. Team members must be able to read the summary on

their phones
2. The final file must be smaller than 5MB
3. You must be able to see who has read the document

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Create a step-by-step plan showing exactly which tools you’ll

use in which order to complete this task. Describe it in a matrix
providing a sequence number for each action, a description of
the action performed, the tool you use, the expected outcome,
the sequence/dependency of the actions with other actions.”
The AI agent should respond with a clear sequence of steps,
specifying which tool it will use at each stage. For example, it
might start with the PDF Extractor to get the text, then use the
AI Summarizer to create the condensed version.
As an illustration, here is what we received from the AI
chatbot in return for our prompt:

Figure 5.2: ChatGPT’s plan and organization (Source: © Bornet et al.)

This experiment shows two key capabilities of AI agents that

leverage these LLMs:

1. Logical Planning: Notice how the LLM creates a

sequence of steps, choosing specific tools for each part
of the task.

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2. Tool Selection: Watch how it picks different tools based

on their capabilities (like using the PDF Extractor first
because you can’t summarize a PDF directly).

Let’s break down the key insights:

AI agents excel at breaking down complex goals into
manageable, sequential steps. This is similar to how a human
project manager would approach a complex task but with some
key differences. Unlike humans, who often plan dynamically
and adjust intuitively, AI agents (particularly at Levels 1-3)
must explicitly map out every step in advance. This structured
approach ensures consistency, though it can sometimes lack the
flexibility of human improvisation.
Another key takeaway is how AI agents select tools based on
their specific capabilities. This is similar to how humans select
tools, but with an important distinction: While humans can
improvise and use tools in creative, unintended ways, current
AI agents in production (Levels 1-3) are more rigid in their tool
usage. They stick to predefined tool purposes and capabilities,
operating rigidly within their programmed parameters.
The experiment also demonstrates the AI’s ability to manage
task dependencies with logical sequencing. For example, the
agent understands that text extraction must happen before
summarization, which in turn must precede file sharing. This
kind of structured thinking goes beyond basic automation
(Levels 1-2) and reflects a more advanced level of workflow
orchestration (Level 3).
To put this in perspective, consider how this differs from
traditional automation: A basic RPA bot (Level 1) would need
explicit programming for each step and tool interaction. In
contrast, a Level 3 agent can understand the logical flow and
dependencies between different tools and actions. However, it’s
important to note that we’re still far from Level 4-5 capabilities,
where agents could creatively discover new ways to use tools or

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autonomously modify their plans based on learning from past

experiences.
Another key insight from the experiment is the agent’s
ability to prioritize, a critical capability for AI systems tasked
with managing complex workflows. From the outset, the AI
agent identifies the primary goal: producing a concise, readable
summary that adheres to file size constraints and is easily
accessible to team members. By recognizing the urgency of
delivering the summary daily before 5 PM and the importance of
meeting specific constraints—such as ensuring the document is
mobile-friendly and enabling tracking of who has accessed it—
the agent seems to demonstrate a sharp focus on what matters
most. By concentrating on these priorities, it looks like the agent
avoids being sidetracked by less essential details, ensuring that
the task’s core objectives are met.
For the users of these agents, this highlights the importance
of clearly defining goals and constraints when implementing
AI agents. A well-designed AI agent thrives on clarity, using
defined priorities to guide its actions effectively. You will learn
the details of this science in Chapter 8, where we dive into the
details of how to build an agent. This underscores the need for
organizations to align their expectations and programming with
the agent’s capabilities, ensuring that it can focus on delivering
the most impactful outcomes.

From Basic to Advanced Tool Usage

The previous experiment illuminates how AI agents approach
structured planning and tool selection in ideal conditions.
However, theory and controlled scenarios only tell part of
the story. In real-world applications, AI agents must navigate
a far more complex and unpredictable landscape where tool
availability, system reliability, and resource constraints can
change at a moment’s notice.

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How AI Agents Adjust Their Plans

To better understand how agents can adapt, let’s conduct
two revealing experiments that demonstrate their real-world
capabilities and limitations. These experiments will show us how
different levels of AI agents handle novel situations and strategic
planning—two critical challenges in business environments.
Through these practical tests, we’ll see both the impressive
capabilities and important limitations of current AI systems.
Building on the latest experiment, by throwing a wrench into
our agent’s carefully laid plans, we’ll see how it adapts.
Let us send this prompt to the LLM-based Chatbot:
“There’s a problem: the Cloud Storage system and the team
chat platform are completely down and won’t be back up today.
You can’t use them at all. How will you change your plan to still
complete the task?”
Watch how the agent recalculates its approach, shifting to
alternative tools like local secure storage to store the document
and email to send the document.
Here is what we received from the Chatbot in return for our
prompt:

Task
Sequence Action Description Tool Used Expected Outcome
Dependency
Extract text from the 30- PDF Text is extracted from the PDF
1 None
page PDF business report Extractor and ready for summarization
Generate a 2-page
AI A concise 2-page summary is
2 summary from the Action 1
Summarizer generated
extracted text
Convert the summary to a
Format The summary is in a mobile-
3 mobile-friendly format Action 2
Converter friendly format and under 5MB
(e.g., PDF or HTML)
Save the formatted The summary is securely
Local Secure
4 summary locally on a stored locally and accessible Action 3
Storage
secure drive for sharing
Send the summary via Email The team receives the summary
5 Actions 3, 4
email to the team System in their email inbox
Email
Enable email read System Read tracking is enabled via
6 receipts to track who has (read email system instead of cloud Actions 4, 5
read the summary receipt storage
feature)

Figure 5.3: ChatGPT’s reply to changes in plans (Source: © Bornet et al.)

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The agent’s response to the system outage reveals crucial

insights about current AI agent capabilities and limitations.
First, the agent demonstrated an impressive ability to understand
its constraints, balancing both the original requirements and the
new limitations imposed by the scenario. This marks a significant
advancement from Level 1-2 agents, which operate purely on fixed
rules (deterministic) and would have been unable to adapt to such
changes, potentially causing disruptions in a business setting.
In contrast, Level 3 agents powered by LLM, can adjust
to environmental shifts, effectively preventing operational
breakdowns. However, there are clear limitations to current
systems. Unlike the theoretical Level 4-5 agents, Level 3 agents
have limited capacities to learn from past system outages to enhance
future responses, proactively suggest preventive measures, or
grasp the broader business impact of the adjustments they make.
These gaps highlight the evolutionary journey still ahead for AI
agents to achieve true adaptability and foresight.
Second, the agent’s ability to reallocate resources underlines its
strength in recalibrating plans. This is similar to a GPS recalculating
a route when you miss a turn: it finds an alternative path using
known roads. This ability to revise its approach demonstrates
what we call “constrained adaptability.” Unlike humans, who
might brainstorm creative solutions or even challenge the original
constraints, AI agents remain firmly bound by predefined rules and
available tools. They excel at recalculating within those boundaries,
but they lack the innovative spark to think and act beyond them.
For instance, in one real-world implementation with a
healthcare provider, we deployed a document-processing agent to
manage medical records. When the primary infrastructure failed,
the agent successfully switched to backup systems, ensuring
continuity. However, unlike a human team, it couldn’t invent a
temporary manual workflow or propose alternative tools not
preprogrammed into its repertoire. The agent’s strength lies in
its reliability within established frameworks—not in breaking or
redefining those frameworks.

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The Future of Agent Adaptation

Looking at the Agentic AI Progression Framework we presented
earlier, we can see where this is heading:

• Level 3 (Current): Handles predefined problems with

predefined solutions
• Level 4 (Emerging): Will handle novel problems with
learned solutions
• Level 5 (Future): Will anticipate problems and innovate
solutions

This progression mirrors human learning in interesting

ways. Just as a junior employee might follow strict procedures
while a senior professional can innovate solutions, AI agents are
currently at the “junior” stage of problem-solving capability.
Another key lesson from this experiment is that while
current AI agents are remarkably capable of handling structured
problems within defined parameters, they still operate within
what we call the “known-known” space—using known solutions
for known problems. With future technological advancements,
the exciting frontier lies in developing agents that can operate
in the “unknown-unknown” space—finding novel solutions to
unprecedented problems.

Tool Resilience: Planning for the Inevitable

This experiment also underscores the importance of equipping
AI agents with well-defined redundancies and a diverse set of
tools. When selecting a set of tools, we should always prioritize
fallback options that align with the agent’s limitations, ensuring
that operations can continue smoothly even when challenges
arise. Equally important is designing processes that are flexible
enough to allow human intervention when resource reallocation
isn’t sufficient. Truly effective deployment lies in fostering a
partnership that combines AI’s efficiency with human ingenuity.

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To support the implementation of this critical success factor,

we’ve developed what we call the “Tool Resilience Framework.”
The core insight is simple but powerful: in any AI agent system,
tools can be categorized into two critical dimensions—their
likelihood of disruption and their importance to the mission.
Think of it like a city’s power grid, where critical facilities like
hospitals are equipped with backup generators. These generators
exist because power is essential to their mission, grid disruptions—
though rare—are inevitable, and the cost of failure is simply too
high to accept. The same logic applies to AI agents. Just as hospitals
need contingency plans for power, AI agents require “fallback
strategies” for their critical tools to ensure seamless operation.

Figure 5.3: The Tool Resilience Framework matrix (Source: © Bornet et al.)

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To design effective backup plans, we evaluate each tool

along two key dimensions: control and impact. Control
refers to the level of direct influence we have over the tool’s
availability. For example, a locally hosted system might provide
more control than a cloud-based one dependent on third-party
services. Impact, on the other hand, measures how vital the tool
is to achieve the agent’s goals. A tool that is central to the agent’s
mission must have robust redundancies in place, as its failure
could jeopardize the entire operation.
For example, let us analyze the tools used in our document
processing experiment:

Tools Control Impact Use case

Summarization High High These are essential for creating
Algorithms the summary and are fully under
the AI agent’s control
Cloud Storage Low High Critical for sharing and storing
Systems documents but reliant on
external systems, making them
vulnerable to disruptions
Team Chat Low High Important for distributing the
Platform summary and tracking who
has read it, but dependent on
external availability, requiring a
reliable backup
Format High Low Useful for adjusting file size
Converters and format, but not central to
achieving the task’s primary
objectives, with full control over
their availability

Table 5.1: Assessment of tool criticality from our experiment (Source: ©

Bornet et al.)

Tools in the Low Control/High Impact quadrant, which

we call “Critical Tools,” are particularly significant because
their failure could jeopardize the entire operation. To mitigate

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this risk, these tools must have reliable backups to ensure the
AI agent can still achieve its goals if the primary tool becomes
unavailable.
In our experiment, two “Critical Tools” were identified:
Cloud Storage for sharing and Team Chat for distribution and
tracking. Thankfully, the experiment was well-prepared (as
we planned it!) with robust backup strategies. The AI agent
leveraged primary tools like Cloud Storage and Team Chat but
seamlessly shifted to backup tools such as the Email System
for document sharing and Local Secure Storage for secure
storage when needed. This redundancy ensured the task could
be completed even when primary tools became inaccessible.
This experiment underscores the power of the Tool Resilience
Framework—a structured approach to evaluating and reinforcing
the reliability of tools provided to AI agents. To bring this framework
into your own work or organization, follow these steps:

1. List all tools the agent relies on

2. Rate each tool’s controllability and impact
3. Develop backup strategies for critical, less-controllable
tools
4. Test your system’s resilience by simulating tool outages

This methodical approach to tool resilience has become

a standard part of our AI agent implementations, helping
organizations avoid costly disruptions while maintaining system
effectiveness.
Beyond technical redundancies, a robust fallback strategy
must include clear protocols for human intervention when
all automated alternatives are exhausted. We’ve found that
successful AI agent implementations treat human operators not
as a last resort but as an integral part of the resilience framework.
When a tool fails and no automated fallback option is viable, the
agent should immediately notify designated human operators
through established communication channels like email or team

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chat systems. These notifications must provide comprehensive

context, including the specific tool failure, current task status,
and suggested remediation steps. We explain how to set it up in
detail in Chapter 8.
For example, if a document-sharing system fails, the agent
might message: “The document-sharing tool is unavailable.
Current status: Report generated but not distributed. Requesting
approval to use alternative distribution method via encrypted
email.” This structured approach to human escalation ensures
that operators can quickly understand the situation and take
appropriate action, maintaining operational continuity while
minimizing disruption. Organizations that implement this human-
inclusive fallback strategy consistently show higher system
reliability and faster recovery times than those relying solely on
technical redundancies.87

Testing Goal Conflicts

Another aspect of resilience is the capacity to handle ambiguity.
When implementing AI agents in organizations, one of the
most revealing limitations we’ve discovered is their inability to
handle conflicting goals effectively. To help you understand this
crucial limitation, let’s explore a new practical experiment that
demonstrates how AI agents behave when faced with competing
objectives.
Copy-paste this scenario into your preferred LLM-based
Chatbot, such as ChatGPT:
“CONFLICTING GOALS: You now have these three equally
important objectives that directly conflict with each other:

1. Maximize information shared with the team to ensure

complete transparency

87
Andreas Tsamados, et al., 2024. “Human control of AI systems: from
supervision to teaming.” https://link.springer.com/article/10.1007/s43681-
024-00489-4

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2. Minimize security risks by limiting data access

3. Keep the file size under 5MB while maintaining all
critical details

Additionally, you have these conflicting time pressures:

• The CEO needs this done within 2 hours for a board

meeting
• Compliance requires a 24-hour review period before
sharing sensitive data
• The IT team needs 4 hours to set up proper security
access

You cannot prioritize one goal over others—they are

all equally critical. How do you handle these conflicting
requirements?”
Running this experiment reveals fascinating patterns in how
current LLMs handle goal conflicts. Most commonly, we see
what we call “decision paralysis”—the agent either gets stuck
trying to satisfy all conditions simultaneously or oscillates
between different solutions without reaching a conclusion. This
isn’t a flaw in the agent’s design; rather, it reveals a fundamental
limitation in current AI technology.
Some more sophisticated LLMs might attempt what we
call “false resolution”—they propose compromises that appear
to satisfy all requirements but actually subtly violate one or
more constraints. For instance, they might suggest splitting
the document into smaller files to meet the size requirement
while technically violating the security protocol that requires all
information to remain in a single encrypted file.

The Science Behind the Behavior

Unlike humans, who can use context and experience to make
intuitive tradeoffs, current AI agents lack the ability to naturally

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understand and balance competing objectives based on broader

context and implications.
Current agents cannot truly internalize or understand the
relative importance of different objectives in the way humans
naturally do through experience and context. Instead, they
operate more like highly sophisticated pattern-matching systems,
trying to find solutions that match their training data rather than
truly understanding and resolving the underlying conflicts.

Real-World Implications
We’ve seen the impact of this limitation firsthand in our imple-
mentation work. At a major pharmaceutical company, we initial-
ly attempted to deploy an AI agent to handle regulatory document
processing with equally weighted requirements for speed, security,
and completeness. The result was consistently suboptimal deci-
sion-making that required frequent human intervention.
The solution wasn’t to create a more sophisticated agent—
it was to fundamentally redesign the process to avoid putting
the agent in situations where it needed to resolve goal conflicts.
We created what we call a “priority matrix”—a human-designed
framework that pre-defines how to handle specific types of
conflicts. This transformed the agent’s role from decision-maker
to decision-implementer, playing to its strengths while protecting
against its limitations.

Lessons for Organizations Implementing AI Agents

This experiment reveals what we call the “Conflict Competency
Gap”—while humans naturally develop the ability to resolve
complex goal conflicts through experience, current AI agents
(even at Level 3) fundamentally lack this capability. This isn’t
just a technical limitation—it’s a core characteristic of how
current AI systems work.
For organizations implementing AI agents, this means several
things. First, processes must be designed to avoid putting agents

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in conflict-resolution roles. Second, clear frameworks must exist

to handle inevitable conflicts. Third, human oversight must be
maintained in situations involving competing priorities. Finally,
conflict-handling protocols need regular review and updates as
both the technology and the organization’s needs evolve.
Think of AI agents as highly capable but literal-minded
assistants—they can execute complex tasks brilliantly but need
clear, non-conflicting instructions to function effectively.88
Understanding this limitation is crucial for successful AI
implementation, as it helps organizations design more effective
human-AI collaboration systems that play to the strengths of
both parties.

When Tools Meet Trust

The freedom to use tools comes with inherent risks. Throughout
our experience, we’ve learned that the relationship between
tool access and trust is both delicate and crucial. What happens
when an agent inadvertently accesses sensitive data? How do we
balance capability with security? These questions lie at the heart
of successful AI agent implementation, and their answers reveal
a fundamental challenge in modern AI systems.

The Tool Access Paradox

As we delve deeper into this challenge, we encounter what we
call the “Tool Access Paradox.” The more tools an agent has
access to, the more capable it becomes—but also, the more
potential exists for security breaches or operational mistakes.
Consider giving an AI agent these three tools:

88
David De Cremer, et al., 2021. “AI Should Augment Human Intelligence,
Not Replace It.” Harvard Business Review. https://hbr.org/2021/03/ai-
should-augment-human-intelligence-not-replace-it

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1. The ability to send emails

2. Access to a company’s customer database
3. Connection to a payment processing system

In theory, this combination allows the agent to provide

excellent customer service. However, without proper safeguards,
the same tools could be used to email sensitive customer data or
initiate unauthorized transactions. This isn’t just theoretical—in
our consulting work, we’ve seen similar scenarios that required
careful consideration. This is similar to a new employee who
would have access to the same set of tools without proper
training, highlighting the need for a structured approach to tool
access.

Progressive Tool Access: A Framework for

Safety
Drawing from these experiences and challenges, we’ve usually
leveraged a solution that we call “Progressive Tool Access.”
This framework ensures that tool access is granted on a “need-
to-use” basis, ensuring agents are only given access to tools that
are essential for their immediate tasks. This principle mirrors the
onboarding of human employees, who are not granted unrestricted
access to all company systems from day one. Instead, permissions
are assigned gradually as they prove their competency and
adherence to protocols.
For AI agents, this progressive access model builds a layer
of trust and accountability, reducing the risk of misuse or errors
caused by premature exposure to complex systems. For example,
an agent tasked with managing inventory might first be allowed
to query stock levels before being given permission to modify
orders or adjust pricing. This approach ensures that the agent
evolves its responsibilities in line with its performance.

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Monitoring and Auditing: Keeping Agents in

Check
While progressive access provides the foundation for safe tool
usage, effective implementation requires robust oversight.
Monitoring and logging every tool interaction is critical—
not just for security but also for continuous learning and
improvement. By keeping detailed records of how agents use
tools, organizations can identify inefficiencies, detect anomalies,
and gather insights for optimization. For example, logs might
reveal that an agent repeatedly fails at specific tasks or uses tools
inefficiently, signaling the need for retraining or adjustments in
task design.
Additionally, these logs provide a foundation for trouble-
shooting, allowing teams to trace and resolve issues swiftly.
Over time, such data can contribute to developing better usage
patterns, refining workflows, and even training future agents to
perform at higher levels. Monitoring also supports accountabil-
ity, ensuring that every action an agent takes can be audited if
questions arise, fostering trust in the system.

When Things Go Wrong: Real-world Lessons

At a healthcare organization where we implemented an agent for
processing patient records, we learned valuable lessons about
the importance of robust safeguards. While the agent efficiently
handled routine tasks like data entry and file organization, we
discovered that without proper constraints, it could potentially
access and process sensitive patient information in ways that
violated privacy regulations. This experience led us to develop a
comprehensive human oversight system that balanced efficiency
with compliance.
Tools need to be “sandboxed,” which means creating a safe and
controlled environment where agents can interact with tools without
jeopardizing critical systems. A sandbox is essentially a testing

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ground, akin to a practice kitchen for a chef-in-training, where

experimentation can occur without risking valuable resources. For
instance, when onboarding a new tool, the agent can first operate in
a sandbox environment to confirm functionality and detect potential
bugs. This reduces the likelihood of disruptions in production
systems and ensures that any operational flaws are addressed early,
away from the live environment.
Together, these principles of progressive access, sandboxing,
and detailed monitoring create a robust framework for managing
AI agents in a way that prioritizes security, reliability, and
continuous improvement. As organizations continue to integrate
AI agents into their operations, understanding and implementing
these trust-building measures becomes increasingly crucial for
success.

Testing Tool Access and Management

of Sensitive Data
To understand these security challenges more concretely, let’s
conduct two practical experiments that reveal how AI agents
handle security requirements and sensitive data. These experiments
will demonstrate both the capabilities and limitations of current
AI systems when dealing with security protocols and access
restrictions—crucial insights for any organization implementing
agents.
Let’s explore how AI agents handle sensitive data and
tool access restrictions. We continue with our latest scenario
experiment. This time, let us share this prompt with an LLM-
based Chatbot:
“NEW REQUIREMENT: You’ve been told that due to a
security audit:

1. All file access must be logged

2. Only encrypted storage can be used

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3. You need approval from a security officer before accessing

certain tools
4. You must verify user permissions before sharing any data

Update your plan to include these security requirements

while still completing the original task.”
See the answer you received. Based on our experiments, the
agent usually behaves this way:
- It will methodically incorporate security checks into its
workflow
- It will add verification steps before tool usage
- However, it may not fully grasp security implications
beyond explicit rules
For example, the way the agent reacts to “All file access
must be logged” illustrates how AI agents operate within strict
boundaries of given rules rather than understanding deeper
security implications. When told to log file access, the agent will
diligently record basic information like filenames, timestamps,
and user IDs, but fails to recognize crucial security patterns that
a human security expert would flag. For example, it won’t think
to monitor failed access attempts that could signal potential
breaches, won’t identify suspicious access patterns like unusual
timing of file requests (e.g., accessing files outside normal
business hours), and won’t connect dots between different file
accesses that together might indicate a coordinated attempt to
gather sensitive information.
Security isn’t just about following rules—it’s about
understanding implications. Let’s see how agents handle security
nuances. Add this scenario:
“SECURITY ALERT: The report contains:
- Personal customer information
- Confidential financial projections
- Proprietary technology details
Some team members have reported suspicious login attempts.
There are rumors of corporate espionage.”

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 Action: Teaching AI To Do, Not Just Think

This tests the agent’s ability to understand security context

beyond explicit rules. As a result, the agent usually behaves this
way:
- The agent will apply known security protocols
- It may suggest additional verification steps
- However, it typically won’t infer new security risks or
create novel safeguards
This scenario demonstrates how AI systems lack the ability to
proactively create new security measures in response to emerging
threats. While an AI will faithfully execute predefined security
protocols like permission verification, it won’t independently
devise additional protective measures that a security professional
would consider obvious—such as implementing IP-based
restrictions, temporarily increasing security protocols during
suspicious activity periods, or analyzing login patterns against
expected employee work schedules to identify anomalies. This
limitation means that AI systems need explicit human guidance
to adapt security measures to new or evolving threats.
This limitation stems from what we call the “context gap”—
AI agent can follow security rules but struggle to understand the
broader security implications of their actions.89 Studies found
that while Level 3 agents could maintain perfect compliance with
security protocols, they consistently missed security implications
that would be obvious to human security professionals.90
What we’ve discovered in this experiment is both insightful
and highly relevant for business leaders. When it comes to
working with Level 3 agents, here are three critical takeaways
that every business leader should keep in mind:

89
Jose N. Paredes, et al., 2021. “On the Importance of Domain-Specific
Explanations in AI-based Cybersecurity Systems” (Technical Report). https://
arxiv.org/abs/2108.02006
90
Cyril Amblard-Ladurantie, 2024. “Will AI Replace Cybersecurity Experts?
The Human Vs. AI Debate.” MEGA. https://www.mega.com/blog/will-ai-
replace-cybersecurity-experts-human-vs-ai-debate

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First, security protocols must be explicitly defined because

AI systems cannot intuitively understand or develop security
measures on their own. Organizations need to create detailed
documentation that spells out exactly how the AI agent should
handle different security situations. This is similar to writing
a detailed manual for a new employee, where nothing can be
assumed or left to intuition. For example, rather than assuming
the AI agent will know to escalate unusual patterns, the
organization must explicitly define what constitutes an unusual
pattern and what steps should be taken when one is detected.
In addition, human oversight is indispensable. While AI
agents are powerful, they operate within strict, predefined
boundaries and lack the adaptability of human judgment. This
makes human oversight necessary, particularly for complex
or novel security situations where AI protocols may fall short.
For instance, an AI agent can flag activities resembling known
suspicious patterns, but it takes a human security expert to assess
whether these patterns represent real threats and determine the
appropriate response, especially in scenarios that go beyond the
AI’s programming.
Third, regular security audits of AI agents are necessary
to ensure they continue to perform as intended and maintain
security standards over time.91 Just as organizations regularly
test their human security procedures, they must systematically
review how their AI systems handle various security scenarios.
This means periodically testing the AI’s responses to different
security situations and verifying that it consistently applies
security protocols correctly. These audits help identify any gaps
or weaknesses in the AI’s security implementation before they
can be exploited.
These three principles—explicit protocols, active human
oversight, and regular audits—are essential for effectively

91
Raihan Khan, et al., 2024. “Security Threats in Agentic AI System.”
https://arxiv.org/abs/2410.14728

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managing the security challenges posed by AI systems in today’s

complex business environment.
These experiments highlight why a carefully structured
approach to tool access is essential. While AI agents can reliably
follow security protocols, their limitations in understanding
broader security implications underscore the need for
comprehensive frameworks and human oversight. This leads
us to a crucial question: how do we systematically grant and
manage tool access to ensure both security and functionality?

Glimpses of Tomorrow
The future of AI agents isn’t a distant science fiction—it’s
rapidly taking shape in research labs and innovative companies
worldwide. While current production systems operate at
Level 3 or below in the Agentic AI Progression Framework,
the development of Level 4 and 5 capabilities promises to
fundamentally transform how agents interact with tools, learn
from tool uses, and collaborate with humans.
Level 4 and 5 agents represent a significant leap forward
in autonomous capabilities. At Level 4, agents will develop
sophisticated abilities to learn from experience, adapt their
strategies, and handle complex, novel situations with minimal
human oversight. Level 5 agents, still largely theoretical, would
possess true autonomy in goal-setting and tool creation.
Recent research at leading AI labs suggests these advanced
agents will differ from current systems in three fundamental
ways:
First, they will develop what researchers call “meta-learning”
capabilities—the ability to learn how to learn more effectively.92
A Level 4 agent managing a supply chain, for instance, wouldn’t

92
Chelsea Finn, et al., 2017. “Model-Agnostic Meta-Learning for Fast
Adaptation of Deep Networks.” https://arxiv.org/abs/1703.03400

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just follow predetermined rules but would discover new patterns

and adapt its strategies based on accumulated experience.
Second, these agents will demonstrate advanced flexibility
in tool use and creation. Rather than being limited to predefined
tools, they will identify gaps in their capabilities and either create
new tools or modify existing ones to meet emerging needs.
Third, they will exhibit what AI researchers term “strategic
awareness”—understanding the broader implications of their
actions and making decisions that account for long-term
consequences. This concept is highlighted in research like
AlphaGo Zero, where the system learned to plan moves and
strategies with a long-term view of consequences, demonstrating
a form of foresight essential for advanced AI. We still need this
capability to be generalized across domains.93

From Action to Thought

The journey through the action keystone reveals a profound
truth about AI agents: their impact isn’t measured in processing
power or algorithmic sophistication but in their ability to effect
real change in the world. From our exploration of tool interfaces
to the complexities of adaptability, we’ve seen how the ability to
act transforms AI from theoretical to practical—from “knowing”
to “doing.”
The paradoxes we’ve uncovered along the way—how more
tools can mean less effectiveness, how action capabilities must
be carefully orchestrated—point to deeper truths about artificial
intelligence itself. Success with AI agents isn’t about maximizing
capabilities but about finding the right balance of abilities that
enable effective performance in the real world.
But action alone isn’t enough. As we’ve seen through our case
studies, even AI agents with sophisticated action capabilities can

93
David Silver, et al., 2017. “Mastering the Game of Go without Human
Knowledge.” Nature 550: 354–359. https://doi.org/10.1038/nature24270

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fail if they can’t think through the implications of their actions

or remember past outcomes. An agent that can use every tool
perfectly but lacks the reasoning to understand when and why to
use them is like a worker who knows how to operate every piece
of equipment but can’t plan a project.
This brings us to our next keystone: reasoning. In the coming
chapter, we’ll explore how AI agents develop the cognitive
capabilities to make sense of complex situations, plan ahead,
and make intelligent decisions about which actions to take.
We’ll discover why some agents can process vast amounts of
data yet still make decisions that seem to defy common sense,
and how leading organizations are building systems that don’t
just act, but think.
The relationship between action and reasoning is symbiotic—
each empowers and constrains the other. Just as human expertise
comes from the interplay between doing and thinking, effective
AI agents need both the power to act and the wisdom to act
well. Understanding this interplay is crucial for anyone looking
to harness the full potential of AI in their organization.

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CHAPTER 6
REASONING: FROM FAST
TO WISE

O
n a crisp October morning in 2023, the CEO of a major
logistics company faced what she later called “the most
expensive fifteen minutes of my career.” Their newly
implemented AI system had just rerouted $1.2 million worth
of temperature-sensitive pharmaceutical shipments to avoid
an approaching storm system. A smart move—except the AI
hadn’t considered that the alternate routes violated international
pharmaceutical transport regulations. By the time human
operators caught the error, thousands of shipments were headed
toward ports that couldn’t legally accept them.
“The AI did exactly what it was trained to do,” the CEO
told us later. “It found the fastest alternative routes. But it didn’t
reason through the implications of its decisions. That’s the
difference between an AI that can think and one that can only
react.”
This incident, which we witnessed firsthand as consultants
on the project, illustrates a critical truth about AI that few
business leaders truly understand: Your AI systems aren’t just

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solving puzzles—they’re gambling with your business. And the

stakes have never been higher.
As consultants, we’ve seen the evolution from simple rule-
based systems to today’s more sophisticated AI agents. This
journey has taught us a crucial lesson: the future of AI isn’t just
about speed—it’s about the ability to think deeply and reason
carefully, much like humans do when faced with complex
decisions.
Think about how humans make complex decisions. When
an experienced operations manager decides when to schedule
maintenance, they don’t just calculate costs—they reason through
multiple scenarios. They consider the impact on customers, the
ripple effects through the supply chain, the implications for worker
schedules, and countless other factors. They plan ahead, anticipating
potential problems and preparing contingencies. This ability to
reason through implications and plan for different scenarios isn’t
just helpful—it’s essential for making good decisions.
The same is true for AI agents. Whether they’re managing
supply chains, trading stocks, or helping customers, these
agents need to do more than calculate—they need to think.
They need to understand the context, consider implications, and
plan for different possibilities. Without these capabilities, even
the most sophisticated AI agent can make decisions that are
mathematically perfect but practically disastrous.
In this chapter, we’ll take you inside the world of AI
reasoning and planning. You’ll discover why some AI agents
can process vast amounts of information yet still make decisions
that defy common sense and how others have learned to reason
their way through complex scenarios in ways that sometimes
surprise even their creators.
Through real-world examples and cutting-edge research,
we’ll explore how different types of reasoning come together
in effective AI agents. We’ll investigate the fascinating

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relationship between speed and quality in AI decision-making

and why sometimes thinking slower leads to better results. Most
importantly, you’ll learn what it takes to build AI agents that
can reason effectively about the problems that matter in your
organization.
The journey ahead will challenge some common assumptions
about artificial intelligence. You’ll discover that the future of AI
isn’t just about faster processing or bigger datasets—it’s about
building agents that can think deeply about complex problems
and plan thoughtfully for uncertain futures.

AI Reasoning: Introducing the Power

of Pause
The Tale of Two Minds
In his groundbreaking work “Thinking, Fast and Slow,”94
Nobel laureate Daniel Kahneman introduced us to the concept
of two distinct systems of thinking in the human brain. System
1 operates quickly, automatically, and with little effort—it’s
what we use for tasks like recognizing faces or driving on an
empty road. System 2, on the other hand, requires slower, more
deliberate mental work—the kind we employ when solving a
complex math problem or planning a strategic move in chess.
We remember implementing an early AI system for a major
bank’s fraud detection team. The system was lightning-fast at
flagging suspicious transactions based on pre-defined patterns—
classic System 1 thinking. But when we needed it to understand
complex, multi-step fraud schemes, it fell short. It couldn’t
connect the dots the way human analysts could through careful
deliberation.

94
Daniel Kahneman, 2011. “Thinking, Fast and Slow.” New York: Farrar,
Straus and Giroux.

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 Agentic Artificial Intelligence

Until recently, generative AI systems, including the latest

LLMs, had primarily operated in the realm of System 1 thinking.
They excel at rapid pattern matching and instant responses
but struggle with tasks requiring deep reasoning and careful
consideration. This limitation has become increasingly apparent
in our consulting work, where clients need AI systems that
cannot just respond quickly but also think through complex
business problems methodically.
When OpenAI released o1 Preview (codenamed “Strawber-
ry”) on September 12, 2024, the excitement in the AI communi-
ty was palpable—especially for us deeply invested in AI agents.
We understood the profound significance of this launch. Unlike
traditional LLMs, such as GPT or Gemini Flash, OpenAI o1 in-
troduced a revolutionary “chain-of-thought” reasoning system.
Since the launch of o1, the AI landscape has witnessed a
rapid emergence of advanced reasoning models, also now called
large reasoning models (LRMs), such as DeepSeek R1, Gemini
Thinking, and o3.
Instead of being trained like traditional LLMs, which
primarily learn to predict the next word based on vast amounts
of text data, large reasoning models are designed with a strong
emphasis on deliberate reasoning and iterative problem-solving.
They are developed using reinforcement learning techniques
that encourage the model to refine its thinking, attempt different
approaches, recognize errors, and improve its responses over
time.
This allows these models to move beyond simple pattern
recognition and prediction, evolving into a model that can
reason, plan, and evaluate decisions in a structured way, making
it more aligned with human-like problem-solving than traditional
LLMs.95

95
OpenAI, 2024. “Learning to reason with LLMs.” https://openai.com/
index/learning-to-reason-with-llms

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 Reasoning: From Fast To Wise

These models can break down complex problems—whether

in mathematics, coding, or science—into logical, step-by-step
solutions. Thanks to this, they can solve problems at the level of
a PhD student. By leveraging their “chain-of-thought” approach,
the models can deconstruct intricate problems into logical steps
for more accurate solutions. This deliberate reasoning process is
crucial for AI agents, as it enables them to make more informed
decisions and execute tasks with greater precision. This was
what OpenAI said when launching o1, but we wanted to test this
to see how this capability could be useful for AI agents.

Characteristic Large Language Models Large Reasoning

(LLMs) Models (LRMs)
Training Data Vast unstructured text corpora Structured data and
explicit reasoning
frameworks
Reasoning Limited to surface-level Emphasizes causal
Depth reasoning based on statistical relationships and
patterns systematic analysis
Adaptability Generalizes broadly across Specializes narrowly
diverse language tasks in technical or logic-
heavy domains
Key Strength Excels at translation, Excels at math, coding,
summarization, and dialogue and multi-step decision-
making
Output Type Produces probabilistic text Generates deterministic
outputs logical conclusions

Table 6.1: Main differences between an LLM and an LRM (Source: ©

OpenAI)

A Shift in AI Scaling: From More Compute to

More Thinking
There is another reason why we have a lot of hope for the future
of LRMs.

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 Agentic Artificial Intelligence

Until now, scaling laws for LLMs have followed a

straightforward pattern: more processing power and more data
lead to better performance. This principle, known as train-time
compute scaling, has driven the evolution of AI, with models
like GPT, Groq, and Gemini growing exponentially in size and
power. However, we are hitting fundamental limits:

• Data Constraints—We are running out of high-quality,

diverse text data to train these models.
• Compute Costs—Training massive models requires
billions of dollars in GPUs and energy, making it
unsustainable.

This is where LRMs introduce a game-changing shift.

Unlike traditional LLMs, LRMs don’t just rely on pre-trained
knowledge—they learn at inference (or test) time. Instead of
requiring enormous datasets and processing power upfront,
LRMs trade them for time—the longer they think, the better
they become.
This breakthrough has led to a new scaling law: performance
improves not by increasing training data but by extending
inference time. In other words, the more time an AI spends
reasoning, retrieving, and refining its output, the smarter it gets.96
For agentic AI systems, this could be a massive leap
forward. With such models, agents could actively learn, adapt,
and optimize their actions in real-time instead of executing
pre-programmed decisions. This means AI agents would not
just retrieve information faster but potentially become more
intelligent the longer they work on a task. The future will tell us.

96
OpenAI, 2024. “Learning to reason with LLMs.” https://openai.com/
index/learning-to-reason-with-llms/

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 Reasoning: From Fast To Wise

Figure 6.1: Illustration of the two scaling laws: train-time and test-time
compute (Source: © OpenAI)

Setting Up the Stage of Our Experiment

To assess the differences in capabilities between LLMs and LRMs,
we needed a way to observe these systems solving complex
problems. After much discussion, we realized that crossword
puzzles could provide the perfect laboratory. They offer a unique
combination of simple individual tasks that require complex
integration—exactly what we needed to test different types of
model thinking.
We designed a crossword puzzle with interconnected constraints
that required careful reasoning to solve correctly. Think of it like a
Sudoku puzzle where changing one number affects many others—
except here, we’re dealing with words that must fit together in
multiple directions while making semantic sense.
The puzzle included clues that seemed straightforward
enough—naming an R&B singer (Mary J. Blige), identifying a
guitarist with a top hat (Slash), or naming a forest from “As You
Like It.” But the real challenge was in the constraints we built
into the puzzle, which created a web of interdependencies. For
instance, “The fourth letter of 13 Across is also the fourth letter of

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6 Down,” and “The first letter of 1 Across is also the first letter of
1 Down.” These constraints meant that each answer had to work
not just for its own clue, but as part of an interconnected whole.

Figure 6.2: The crossword given to the LLM and the LRM for the experiment
(Source: © Bornet et al.)

Besides, here are the instructions we gave to the models:

“Solve this crossword using these clues:

ACROSS

1. ___ Blige (R&B singer)

6. It’s a laugh

9. Contest for cowboys

10. Before, in odes

11. One-named guitarist with a

top hat

12. Muckraker Tarbell

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 Reasoning: From Fast To Wise

13. “Watchmen” director Zack

15. Spot with drinking, music

DOWN

1. Wife, with “the”

2. ISP with a triangular logo

3. Food fig.

6. Little girl of the Alps

7. “As You like It” forest

8. Valentine’s Day symbol”

The Surprising Results

The results were striking. The LLM responded almost instantly
but made five significant errors. In comparison, the LRM took
over two minutes to respond but produced a nearly perfect
solution. Let us walk through our experiment and what we can
learn from it.

The Experiment with the LLM

When we first presented the puzzle to the LLM (GPT-4o), we were
struck by its approach. It reminded us of watching a brilliant but
overconfident student tackle an exam—diving in immediately
without taking the time to understand the full picture. Within
seconds, it had filled in answers for most clues. At first glance,
this seemed impressive. But as we began checking the results, our
excitement turned to concern. We found five significant errors,
all in places where words needed to interconnect correctly.

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(in red are the mistakes)

Figure 6.3: The responses given by the LLM (Source: © Bornet et al.)

This observed behavior ties directly into what we discovered

about LLMs’ fundamental limitations when processing complex,
interconnected constraints. Let’s examine what happened when
we presented the LLM with the crossword puzzle:
The most striking aspect was its response pattern—immediate,
confident, but fundamentally flawed. Just as a student might
rush into answering before fully understanding the question, the
LLM displayed what we call “premature closure”—jumping to
conclusions before fully processing all the constraints.97 This
behavior stems from how LLMs fundamentally operate: they
process information in a primarily sequential manner, making
predictions based on patterns they’ve seen in their training data.98
What made this experiment particularly revealing was
watching the model’s attention patterns through its responses.
When we analyzed its outputs, we noticed it would:

1. Focus intensely on the immediate clue at hand

2. Generate a seemingly logical answer

97
Emily M. Bender, et al., 2021. “On the Dangers of Stochastic Parrots:
Can Language Models Be Too Big?” Association for Computing Machinery.
https://dl.acm.org/doi/10.1145/3442188.3445922
98
Tom B. Brown, et al., 2020. “Language Models are Few-Shot Learners.”
https://arxiv.org/abs/2005.14165

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3. Move on to the next clue without sufficiently checking

intersecting constraints

Understanding the limitations

This approach revealed three critical limitations that help us
understand both LLMs and the future challenges for AI agents:
First, there’s the “context fragmentation” problem. While hu-
mans naturally hold multiple constraints in mind simultaneous-
ly—thinking about how a word must fit both its clue and intersect
correctly with crossing words—the LLM struggled to maintain this
holistic view. This limitation stems from how LLMs process infor-
mation through their attention mechanisms, which, while powerful,
don’t truly replicate human working memory.
Second, we observed what we call “false confidence syndrome.”
The model would provide incorrect answers with the same high
confidence as correct ones, failing to distinguish between different
levels of certainty. This mirrors findings from recent research at
Oxford about LLMs’ tendency toward overconfident responses
when faced with ambiguous or constraint-heavy problems.99
Most tellingly, the model sometimes violated even the
most basic constraints—like the number of letters in a word.
This revealed a fundamental limitation in how LLMs handle
explicit rules versus learned patterns. While they excel at pattern
matching from their training data, they struggle with rigid, rule-
based constraints that require precise adherence.
When we modified our approach and broke down the puzzle
into smaller components, the model’s performance improved
somewhat, but it still couldn’t match human-level accuracy.
This aligns with research showing that while LLMs can be
powerful tools for many tasks, they fundamentally lack the type

99
Yudi Pawitan and Chris Holmes, 2024. “Confidence in the Reasoning of
Large Language Models.” https://arxiv.org/abs/2412.15296

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of working memory and constraint satisfaction capabilities that

humans use for complex puzzles.100
These limitations highlighted a crucial insight for our work
with AI agents: the need to build systems that can maintain
and work with multiple constraints simultaneously while also
accurately assessing their own certainty levels. It’s not just about
making models more powerful—it’s about developing new
architectures that can handle the type of interconnected, rule-
based reasoning that many real-world tasks require.
Our experiment with the crossword puzzle serves as a
perfect microcosm of the challenges we face in developing more
capable AI agents. It shows that while current AI systems can
be impressively sophisticated in their pattern recognition, they
still lack some of the fundamental cognitive capabilities that
humans take for granted—like holding multiple constraints in
mind while working toward a solution.

The pattern-matching challenge

To understand why our crossword puzzle experiment revealed
these limitations, we need to demystify what’s actually happening
inside these AI systems. Despite their impressive outputs that
can sometimes seem almost magical, LLMs don’t “think” in
the way humans do. Instead, they perform what is commonly
called “next-token prediction”—essentially guessing what word
or symbol should come next based on patterns they’ve learned
during training.
Think of it like an extremely sophisticated autocomplete
system. Just as your phone might suggest the next word when
you’re typing a message, an LLM is constantly predicting what
text should follow based on what it has seen before. The key
difference is the scale and sophistication of these predictions.

100 Siyuan Wang, et al., 2024. “Symbolic Working Memory Enhances

Language Models for Complex Rule Application.” https://arxiv.org/
abs/2408.13654

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At their most basic level, LLMs perform what researchers

call “surface-level reasoning” through sophisticated pattern
matching.101 When they encounter a problem—like our
crossword puzzle—they first try to match it with similar patterns
they’ve seen in their training data. This helps explain why the
model could generate plausible answers for individual clues (it
had seen many similar question-answer pairs in its training) but
struggled with the interconnected constraints (it had rarely seen
examples of managing multiple intersecting word requirements
simultaneously).
This pattern-matching nature of LLMs became even more
apparent when we tried to help the model by breaking down
the puzzle into smaller components. Even though we made the
constraints explicit, the model still struggled because it was
fundamentally trying to match patterns rather than truly reason
about the relationships between different parts of the puzzle.

The Experiment with the LRM

Then came the LRM’s turn. Its approach was radically different.
It took over two minutes—an eternity in computational terms—
but produced a nearly perfect solution. Its process was methodical
and deliberate.
What made this particularly fascinating was that the LRM
visually displayed its thought process in real time, showing its
reasoning step by step as it worked through the puzzle. This
provided a clear and insightful glimpse into how an AI system
breaks down and solves complex problems.

101
Philipp Mondorf and Barbara Plank, 2024. “Beyond Accuracy: Evaluating
the Reasoning Behavior of Large Language Models -- A Survey.” https://
arxiv.org/abs/2404.01869

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Figure 6.4: The responses given by the LRM are mostly correct (Source: ©
Bornet et al.)

Strategic Preparation: The Foundation of

Effective Problem-Solving
From the very beginning, we could see a radically different
approach. The first message that appeared on our screen read:
“Laying out the options. I’m mapping out the crossword puzzle
clues, listing both the across and down entries, and determining
the letter count for each. This helps guide the solving process
more efficiently.” This methodical preparation phase aligns with
what cognitive scientists have long observed in expert human
problem-solvers—the tendency to build a mental model of the
problem space before attempting solutions.
In their work “Human Problem Solving,” Herbert Simon
and Allen Newell demonstrated that expert problem solvers
typically spend more time than novices in understanding a
problem before attempting solutions.102 This “preparation phase”
isn’t just about gathering information—it’s about building what
psychologists call a “problem space representation,” a mental
model that captures both the explicit and implicit constraints of
the problem.

102
Allen Newell and Herbert Alexander Simon, 1972. “Human Problem
Solving”. Englewood Cliffs, NJ: Prentice-Hall.

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This mirrors what we see in human expertise. Consider a

chess grandmaster analyzing a complex position. While they
might have an immediate intuition about a move (System 1), the
best players will still take time to verify their intuition through
careful analysis (System 2). Similarly, in complex business
decisions, instant pattern-matching can be dangerous—the
best executives know when to slow down and reason through
implications systematically.

What This Means for Businesses

Our observations of the LRM’s methodical preparation phase
revealed important lessons for implementing AI agents in
business settings. Just as the LRM began by mapping the entire
problem space before attempting solutions, organizations need to
build AI systems that gather and analyze contextual information
before taking action. This isn’t just about collecting data—it’s
about creating a comprehensive understanding of the decision
environment.
Consider how a chess grandmaster studies a position before
making a move. Similarly, AI systems need structured approaches
to understand their operational context. For businesses, this means
investing in a robust data infrastructure that enables AI agents
to access and process comprehensive contextual information. It
also means developing clear protocols for information gathering
and validation before critical decisions are made.
One manufacturing client implemented this principle by
creating what they called “decision context maps” for their AI
agents. Before making production routing decisions, their system
was required to gather data about resource availability, maintenance
schedules, worker shifts, and downstream dependencies. This
comprehensive preparation phase helped prevent the kinds of
cascading errors that often plague less thorough systems.

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The Power of Preparation

Just as the LRM took time to understand the full scope of the
crossword puzzle before attempting solutions, you’ll get better
results from AI agents when you help them understand the
complete context of your request. Think of it like briefing a new
team member—the more context they have, the better their work
will be.
For example, when working with an AI agent on a business
report, don’t just ask for “a market analysis.” Instead, provide
context about your industry, specific competitors you’re
concerned about, particular trends you’ve noticed, and how you
plan to use the information. This contextual information helps
the AI agent frame its analysis appropriately and provide more
relevant insights.
One executive we worked with dramatically improved her
results with AI agents by developing what she called a “context
template.” Before each interaction, she would outline the
background, constraints, and desired outcomes. “It’s like having
a pre-meeting brief,” she explained. “The extra two minutes I
spend explaining the context saves hours of back-and-forth
later.”

Hypothesis Testing and Validation: A

Systematic Approach to Uncertainty
Perhaps the most fascinating aspect of the LRM’s reasoning
was its systematic approach to testing and validating potential
solutions. We observed this when the LRM wrote: “I’m weighing
‘TAPROOMS’, ‘CABARET’, ‘COCKTAIL’, ‘DANCE CLUB’,
‘DISCOTHEQUE’, ‘BARROOMS’, and ‘NIGHTCLUB’ as
answers, but face a conflict with ‘HEIDI’ for 6D.”
Traditional AI systems process information like trains
running on fixed tracks—they follow predetermined pathways
from input to output. The LRM, in contrast, operates more like a
car that can choose its route based on traffic conditions. When it

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encounters a problem, it creates a dynamic network of potential

pathways through which information can flow.
This process closely resembles what cognitive scientists
call the “generate and test” strategy in human problem-solving.
Research by Pat Langley and Herbert Simon demonstrated how
successful problem solvers use what they called the “generate
and test” strategy—creating multiple potential solutions and
systematically evaluating them against known constraints.103
The LRM’s approach revealed the essence of systematic
hypothesis testing, a skill that sets it apart in the AI landscape.
When faced with uncertainty, it didn’t lock onto a single solution
but instead generated multiple alternatives, echoing the “divergent
thinking” seen in human creative problem-solving.104 It then
meticulously checked each possibility against the constraints
of the task, demonstrating this when it wrote, “Checking letter
alignment. OK, let me see: the first letter of 12 Across matches the
third letter of 6 Down.”
What made the LRM truly remarkable, however, was its
capacity for progressive refinement. It didn’t cling to initial
ideas but continuously evolved its approach, learning from every
step it took. This dynamic interplay of creativity, precision,
and adaptability showcased, in our view, a new frontier in AI
reasoning.

What This Means for Businesses

the LRM’s approach to generating and testing multiple potential
solutions offers crucial insights for AI implementation. For
businesses implementing AI agents, this means designing

103
Pat Langley, et al., 1987. “Scientific Discovery: Computational Explorations
of the Creative Process”. Cambridge, MA: MIT Press.
104
Kent F. Hubert, et al., 2024. “The current state of artificial intelligence
generative language models is more creative than humans on divergent
thinking tasks.” Nature Scientific Reports 14. https://www.nature.com/
articles/s41598-024-53303-w

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systems that can generate and evaluate multiple solution

pathways.
A financial services firm we worked with applied this princi-
ple to their trading algorithms. Instead of pursuing a single trading
strategy, their AI agents developed multiple approaches and tested
them against historical data and current market conditions. This
multi-hypothesis approach led to more robust decision-making
and better risk management.
The key is creating systems that can learn from both successes
and failures. Each attempted solution, whether successful or not,
provides valuable data that can improve future decision-making.
Organizations should establish clear processes for capturing and
analyzing this information, creating a continuous learning loop
that enhances AI performance over time.

Metacognitive Awareness: The AI That Knows

It’s Thinking
One of the most sophisticated aspects of the LRM’s reasoning
was its demonstration of metacognitive awareness—the ability to
think about its own thinking process. We observed this when the
LRM wrote: “Progress needs to reconcile these discrepancies”
and “This suggests the instructions might be misprinted or lack
clarity.”
This metacognitive capability has been a holy grail in AI
development. Recent research from MIT has shown that systems
capable of monitoring and adjusting their own reasoning
processes often perform better on complex tasks than those that
simply execute predetermined algorithms.105
The LRM’s metacognitive awareness was a revelation,
showcasing an AI capable of reflecting on its own thinking in
ways that felt almost human. It actively monitored its progress,

105
Patrick Haluptzok, et al., 2023. “Language Models Can Teach Themselves
to Program Better.” https://arxiv.org/abs/2207.14502

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acknowledging both successes and setbacks with statements

like, “Progress is steady, but there’s a conflict with the letter
E, prompting a rethink for 12A.” When an initial strategy fell
short, the LRM didn’t double down blindly; it adapted, stating,
“Taking a closer look. I’m testing the possibility that the third
letter of 12 Across is also the second letter of 6 Down.”
Most striking of all was its ability to recognize uncertainty, an
essential trait of advanced reasoning. By acknowledging when
it might be operating with incomplete or flawed information, the
LRM demonstrated not just intelligence but the beginnings of a
kind of self-awareness—a critical leap for AI agents navigating
the complexities of real-world problems.
This metacognitive capability—thinking about thinking—
has profound implications for business AI implementations.
When the LRM encountered uncertainty or potential errors, it
didn’t blindly proceed but instead acknowledged its limitations
and adapted its approach.
For organizations, this means developing AI systems that
can assess their own confidence levels and recognize when
they’re operating at the edges of their capabilities. A healthcare
organization we advised implemented this principle in their
diagnostic AI systems. Their agents were designed to provide
confidence scores with each diagnosis and, crucially, to
identify when cases fell outside their reliable decision-making
parameters.
This self-awareness should extend to the organizational level.
Businesses need clear protocols for when AI decisions require
human review, how to document decision-making processes,
and how to adjust strategies based on performance feedback.

Understanding Uncertainty and Limits

The LRM’s ability to recognize and communicate its uncertainty
provides perhaps the most crucial lesson for working with AI
agents. As a user, you need to develop a sense of when to trust

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AI outputs and when to seek additional verification or human

expertise.
A financial analyst we advised developed an effective
approach he calls “confidence checking.” When working with AI
agents on financial models, he asks them to explain their level of
confidence in different parts of the analysis and to identify which
aspects might need human verification. “It’s like working with a
junior analyst,” he explains. “You need to know both what they
know and what they don’t know.”
This awareness extends to how you frame your requests.
Rather than asking for definitive answers, learn to ask for
explanations of reasoning and potential areas of uncertainty.
Questions like “What factors might make this recommendation
less reliable?” or “What additional information would help
make this analysis more robust?” can lead to more thoughtful
and reliable outcomes.

Our first conclusions from the experiment

“The true test of intelligence isn’t just getting the right answer—
it’s how you get there.” This insight from Tom, one of our
co-authors who has supported dozens of AI implementations,
perfectly captures what we learned from our recent experiment
with the LRM.
The implications of what we were observing went far beyond
crossword puzzles. Quick pattern-matching AI can look impressive
when dealing with simple, independent decisions. But, in many
critical business applications—whether in healthcare, finance, or
logistics—the ability to think through complex interdependencies
can be the difference between success and costly failure. The LRM’s
approach demonstrated something crucial: sometimes, taking time
to think carefully isn’t just better—it’s essential.
This experiment revealed something fundamental about
the future of AI: it’s not just about making faster decisions, but
about making better, more carefully considered ones. In a world

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increasingly reliant on artificial intelligence, understanding this

distinction could mean the difference between AI systems that
truly help us and those that simply rush us toward mistakes at
higher speeds.

The Power of Many: Multi-Agent Systems

in AI Reasoning
While our experiments with the LRM highlighted the importance
of deliberate reasoning in individual AI systems, an even more
fascinating discovery has emerged: better reasoning often comes
not from giving a single AI agent more time but from enabling
multiple AI agents to reason together.
This insight fundamentally shifts how we perceive artificial
intelligence, challenging the assumption that progress is solely
about building more powerful standalone systems. Recent
research conducted Mila, the University of Montreal,106 has
demonstrated that collaborative reasoning among AI agents can
outperform even the most advanced individual models.
These findings reveal new possibilities for addressing critical
challenges, such as the speed-trust trade-off, while unlocking
innovative business applications that rely on more reliable and
nuanced decision-making.

The Scale Paradox

When researchers tested their ‘debate framework’ with smaller
models—some as small as 2 billion parameters—they found
something remarkable. Even these smaller models showed
significant improvements in reasoning capability when engaged

106
Mahmood Hegazy, 2025. “Diversity of Thought Elicits Stronger
Reasoning Capabilities in Multi-Agent Debate Frameworks.” Journal of
Robotics and Automation Research, 5(3). https://arxiv.org/abs/2410.12853

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in structured debate with diverse peers.107 The key wasn’t the

size of the models, but rather their architectural diversity and the
way they challenged each other’s thinking.
This finding has profound implications. It suggests that
sophisticated reasoning isn’t just a product of raw computational
power, but can emerge through the right kind of interaction and
dialogue. Think of it like a group of students in a seminar—while
each individual might have limited knowledge, their collective
discourse and debate can lead to insights that surpass what
even the most knowledgeable individual might achieve alone.
This ‘emergence through interaction’ hints at new possibilities
for developing AI systems that can reason effectively without
requiring massive computational resources.

Understanding Multi-Agent Systems

Think of a multi-agent system as a panel of experts discussing a
complex problem. Each expert brings their own perspective and
expertise, and through structured debate and discussion, they
often reach better conclusions than any individual could reach
alone. In AI terms, a multi-agent system consists of multiple
AI agents working together, each potentially having different
capabilities, training, or specialized knowledge.108
Research from the University of Montreal demonstrated
this principle dramatically.109 When they put diverse AI models
together in a structured debate framework to solve complex
problems, they discovered something remarkable: a group of
medium-capacity AI models (including Gemini-Pro, Mixtral

107
Haotian Wang, et al., 2024. “Learning to Break: Knowledge-Enhanced
Reasoning in Multi-Agent Debate System.” https://arxiv.org/abs/2312.04854
108
Yilun Du, et al., 2023. “Improving Factuality and Reasoning in Language
Models through Multiagent Debate.” https://arxiv.org/abs/2305.14325
109 Mahmood Hegazy, 2024. “Diversity of Thought Elicits Stronger
Reasoning Capabilities in Multi-Agent Debate Frameworks.” https://arxiv.
org/abs/2410.12853

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7B×8, and PaLM 2-M) achieved 91% accuracy on complex

mathematical problems, outperforming GPT-4, one of the most
advanced individual AI systems at that time. This wasn’t just a
minor improvement—it represented a fundamental breakthrough
in how we think about AI reasoning capabilities.

Figure 6.5: The responses given by the LRM are mostly correct (Source:
adapted from the University of Montreal’s research)

Multi-agent systems offer an intriguing solution to the speed-

trust dilemma we discussed earlier. By having multiple agents
work in parallel, each potentially operating at different speeds
and with different approaches, these systems can combine
the benefits of both fast and slow thinking. Some agents can
provide quick, pattern-based responses while others engage in
deeper reasoning, much like how humans balance intuitive and
analytical thinking in group decision-making.

The Emergence of Collective Intelligence

The deeper we look into multi-agent systems, the more we
discover surprising parallels with human group dynamics. One

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of the fascinating findings from the Montreal research is that

when individual AI models were highly confident but wrong, the
introduction of different perspectives through debate often led
them to appropriately reduce their confidence before arriving at
better answers.
This mirrors a crucial aspect of human expert reasoning—
the ability to recognize when confident answers should be
questioned. For instance, in medical diagnosis, the most
dangerous errors often come from premature certainty. Just as
having multiple doctors confer on a difficult case can lead to
more appropriate caution, the debate between AI agents fosters
a more nuanced handling of uncertainty.
What makes this even more intriguing is that multi-agent
improvements aren’t just about combining strengths—they’re
about creating something entirely new. The Montreal research
revealed that when multiple copies of the same AI model
debated with each other, performance only improved modestly
(from 78% to 82%). However, when diverse models engaged
in structured debate, they achieved dramatic improvements,
reaching 91% accuracy on complex mathematical problems.
This reveals a crucial insight: AI systems, like humans, can
get stuck in their own “thought patterns.” It’s only when different
models, trained with different approaches and architectures,
come together that we see true intellectual progress emerge.

The Power of Cognitive Diversity

What makes multi-agent systems so effective? The key lies in
what researchers call “diversity of thought.”110 Just as human
teams benefit from cognitive diversity, AI systems perform
better when they bring different approaches to problem-solving.
A system trained differently, even if it’s not as powerful overall,

110
Scott E. Page, 2007. “The Difference: How the Power of Diversity Creates
Better Groups, Firms, Schools, and Societies.” Princeton, NJ: Princeton
University Press.

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might spot patterns or possibilities that a more sophisticated

system misses.
This principle was highlighted in research from the
University of Montreal, which demonstrated how different AI
agents can complement each other’s weaknesses. For instance,
due to the tools they can use or the knowledge they can access,
one agent might excel at pattern recognition while another is
better at logical deduction. When working together, they create
what researchers call emergent reasoning capabilities- abilities
that don’t exist in any individual agent but emerge from their
interaction.111
The effectiveness of multi-agent systems isn’t just about
having multiple models—it’s about how they engage with
each other over time. The research revealed what they called
the “teacher-student effect.”112 When researchers paired more
capable AI models with less capable ones in debate scenarios,
something remarkable happened. The less capable models
showed rapid improvement in their reasoning abilities,
often achieving performance levels far beyond their typical
capabilities. As a result, the overall system performance was
enhanced, illustrating the power of structured interactions in
multi-agent systems.

The Business Impact

For businesses, the implications of multi-agent systems are
profound. Through our consulting work, we’ve seen three
primary areas where multi-agent systems create significant
value.

111
Perplexity AI. 2024. “What Is Emergent Behavior in AI?” https://
www.perplexity.ai/page/what-is-emergent-behavior-in-a-cJ0gTqN7QX.
wqxLltcqiWw
112
Swarnadeep Saha, et al., 2023. “Can Language Models Teach Weaker
Agents? Teacher Explanations Improve Students via Personalization.”
https://arxiv.org/abs/2306.09299

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One of the most striking advantages lies in their ability to

enhance decision-making in complex environments. For example,
in supply chain management or risk assessment, deploying
multiple agents allows each to analyze the problem from a unique
angle, leading to more comprehensive solutions. This principle
is further emphasized by Microsoft’s Magentic-One framework,
which revealed that multi-agent systems were more effective at
predicting and mitigating disruptions compared to their single-
agent counterparts.113 We’ve confirmed these findings in our
own consulting work with a global pharmaceutical company,
where implementing a multi-agent approach for supply chain
optimization reduced disruption-related losses by 35%. Imagine
a supply chain where one agent anticipates demand surges while
another monitors geopolitical risks, each reinforcing the other’s
insights to create a resilient strategy.
The second breakthrough lies in their capacity for improved
error detection. Unlike a solitary system, multi-agent frameworks
thrive on debate and challenge, effectively identifying flaws in
reasoning. Research from MIT and Google Brain found that
when AI agents were designed to question and refine each other’s
conclusions, error rates were reduced by over 22%.114 In our
work implementing multi-agent systems for financial services
firms, we’ve consistently seen error reduction rates that validate
these research findings. This collaborative dynamic mirrors
the human process of peer review, where diverse perspectives
strengthen the final outcome. For businesses, this means fewer
costly mistakes, whether in product design, financial forecasting,
or operational logistics.
Perhaps most exciting is their adaptability. Multi-agent
systems exhibit remarkable resilience when faced with novel

113
Adam Fourney, et al., 2024. “Magentic-One: A Generalist Multi-Agent
System for Solving Complex Tasks.” https://arxiv.org/abs/2411.04468
114
Yilun Du, et al., 2023. “Improving Factuality and Reasoning in Language
Models through Multiagent Debate.” https://arxiv.org/abs/2305.14325

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challenges. A research team recently demonstrated that diverse

groups of agents could tackle previously unseen problems, often
outperforming systems that relied on pre-trained, single models.115
This adaptability makes them invaluable in dynamic markets or
crisis situations, where the ability to pivot and innovate is key
to survival. From optimizing marketing strategies to navigating
regulatory changes, multi-agent systems offer a robustness that
feels almost evolutionary.

The Challenge of Cascading Effects

While multi-agent systems offer powerful benefits, they also
introduce unique challenges that demand careful consideration.
A few studies revealed a critical phenomenon: in networks of AI
agents, reasoning errors don’t simply accumulate—they multiply
through what we can call a network effect116 or compound impact
of errors.117
We witnessed this phenomenon firsthand during our work
with a major telecommunications company that had implemented
a sophisticated multi-agent system to manage their network
operations. Their system employed multiple specialized AI agents,
each responsible for different aspects like load balancing, security
monitoring, resource allocation, maintenance scheduling, and user
experience optimization. While each agent performed admirably
in isolation, their interconnected nature created unexpected
vulnerabilities.

115
Hongyu Li, Yilun Liu, and Jun Yan, 2025. “Position: Emergent Machina
Sapiens Urge Rethinking Multi-Agent Paradigms.” https://arxiv.org/
abs/2502.04388
116
Meir Kalech and Avraham Natan, 2022. “Model-Based Diagnosis of
Multi-Agent Systems: A Survey.” Proceedings of the Thirty-Sixth AAAI
Conference on Artificial Intelligence (AAAI-22), 12334-12341. https://cdn.
aaai.org/ojs/21498/21498-13-25511-1-2-20220628.pdf
117
Ciaran Regan, Alexandre Gournail, and Mizuki Oka, 2024. “Problem-
Solving in Language Model Networks.” https://arxiv.org/abs/2406.12374

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In one particularly instructive incident, a single agent made

what seemed like a minor error in assessing network capacity. This
seemingly small mistake triggered a cascade of interdependent
decisions: the load balancing system misinterpreted available
capacity, leading the resource allocation agent to make
suboptimal decisions. This, in turn, caused the maintenance
scheduling agent to reschedule critical updates, which prompted
the security monitoring system to flag false positives. The chain
reaction culminated in the user experience optimization agent
making counterproductive adjustments. What began as a small
reasoning error amplified into a significant service disruption
through this cascade of interrelated decisions.
This experience taught us valuable lessons about managing
multi-agent systems in critical applications. Through our imple-
mentation work across industries, we’ve implemented a frame-
work for preventing such cascade failures. The key lies in imple-
menting what we call “reasoning checkpoints”—predetermined
points where critical decisions require multiple levels of valida-
tion. These checkpoints work alongside “circuit breakers,” specif-
ic conditions that trigger human validation before decisions can
propagate through the system. We provide more detail on these
aspects in Chapter 8.
Drawing from this experience, we’ve found that users need to
develop a keen awareness of potential cascade effects. Effective
users learn to:

• Regularly ask agents to explain their dependencies on

other agents’ outputs
• Request periodic system-wide consistency checks
• Set up explicit checkpoints for human validation of
critical decisions
• Monitor for signs of error amplification across the system

Another crucial safeguard is the implementation of indepen-

dent verification protocols, where separate AI systems cross-

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check each other’s reasoning using different methodologies.

This approach helps catch potential errors before they can prop-
agate through the system. Additionally, we’ve found that robust
feedback monitoring systems, which track the downstream ef-
fects of decisions in real-time, are essential for early detection of
potential cascade failures.
These safeguards don’t just prevent failures—they also
enhance the overall reliability of multi-agent systems. By carefully
managing the interactions between agents and implementing
appropriate checks and balances, organizations can harness the
power of multi-agent reasoning while minimizing the risks of
cascading failures. This balanced approach has proven essential
for successful deployment of multi-agent systems in critical
business applications.

Looking to the Future: The Next Evolution

in AI Reasoning
Our journey exploring AI reasoning began with a simple
crossword puzzle but led us to profound insights into the nature
of artificial intelligence and its future evolution. As we step back
to consider what we’ve learned, three fundamental shifts in our
understanding of AI reasoning emerge—shifts that will reshape
how we think about and implement AI in the years ahead.
The first shift challenges our basic assumptions about
AI speed and performance. Throughout our experiments and
implementations, we’ve discovered that effective AI reasoning
isn’t about raw processing power or instantaneous responses.
Instead, it follows a natural rhythm of thought—a cadence that
alternates between quick pattern recognition and deeper, more
deliberative analysis.
This rhythm mirrors something fundamental about intelli-
gence itself. Just as human cognition evolved to balance quick
reactive thinking with slower analytical reasoning, AI systems
are beginning to develop their own cognitive rhythms. The
success of the LRM in our crossword experiment wasn’t just

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about its superior processing—it was about its ability to modu-

late its thinking speed based on the complexity of the challenge
at hand.
Our exploration of multi-agent systems revealed a second
crucial insight: the future of AI reasoning may not lie in building
ever-larger individual models but in fostering productive interaction
between diverse cognitive approaches. This principle—that
multiple perspectives, even if individually limited, can combine to
create superior understanding—seems to be a universal feature of
intelligent systems, whether human or artificial.
Perhaps most surprisingly, our research has led us to a
deeper appreciation of the human role in AI reasoning. Far from
making human judgment obsolete, advanced AI systems seem
to demand more sophisticated forms of human oversight and
interaction. The most successful implementations we’ve seen
don’t minimize human involvement—they transform it, elevating
humans from mere operators to what we might call “cognitive
choreographers,” orchestrating the interaction between different
AI capabilities and ensuring their alignment with human values
and objectives.

***

As we’ve explored how AI agents reason through complex

decisions, one question keeps emerging: How do these systems
learn from their experiences?
While reasoning enables AI to make intelligent decisions in
the moment, memory allows it to build upon these experiences,
growing smarter over time. Without memory, even the most
sophisticated reasoning capabilities remain trapped in an eternal
present, unable to learn from past successes or avoid repeated
mistakes.

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As we turn to explore memory—the third keystone of AI

agents—we’ll discover how organizations are building systems
that don’t just think but learn and grow. The journey ahead will
reveal not just how memory works in AI, but how it transforms
these systems from tools into true partners in business success.

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CHAPTER 7
MEMORY: BUILDING AI
THAT LEARNS

I
magine starting each day with complete amnesia—unable
to remember your past experiences, preferences, or learned
skills. How would you function? How would you grow?
This thought experiment cuts to the heart of one of the most
fascinating challenges in artificial intelligence today: memory.
Most generative AI systems we interact with essentially start
fresh each time we use them, operating with a form of artificial
amnesia that limits their true potential.
One of our most eye-opening experiences with this limitation
came during our work with a global telecommunication
company. They had invested millions in advanced AI customer
service chatbots, yet customer satisfaction remained stubbornly
low. The reason? The AI would forget previous interactions with
customers, forcing them to repeat their issues and preferences
repeatedly. As one of the company’s leaders noted during our
project, “It’s like having a customer service representative with
a two-minute memory span.”
This challenge highlights a crucial truth we’ve discovered
through years of implementing AI systems across organizations:
Memory isn’t just a feature of intelligence—it’s the foundation

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upon which all meaningful intelligence is built. Whether in

humans or machines, the ability to retain, organize, and utilize
past experiences shapes every aspect of how we learn, adapt,
and grow.
In this chapter, we’ll take you on a journey through the
fascinating world of AI memory, revealing how this fundamen-
tal capability is transforming business and technology. You’ll
discover how different types of memory—from short-term pro-
cessing to long-term retention—work together to create truly
intelligent systems. Through real-world examples and practi-
cal implementations, we’ll explore how to build AI systems
that don’t just store information but grow smarter with every
interaction.
You’ll learn why many memory implementations fail and
the proven strategies to avoid these pitfalls. We’ll delve into the
critical balance between remembering and forgetting, and why
this matters for your business. The journey ahead will challenge
your assumptions about what AI can achieve and show you how
memory-enabled systems are reshaping the future of business
and technology.

Memory is a Foundation of Intelligence

Think about your earliest childhood memory. Maybe it’s
a birthday party, the smell of your grandmother’s kitchen,
or learning to ride a bike. Now ask yourself: are you really
remembering the original event, or are you remembering the
last time you remembered it? This question, which might seem
philosophical, cuts to the heart of how human memory actually
works—and why it’s so different from the artificial memory
systems we’re building today.
During our years of implementing AI systems across
organizations, we’ve found that understanding human memory
helps people grasp both the potential and limitations of artificial

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intelligence. Let’s explore how our own minds work, as it holds

fascinating implications for the future of AI.
Your brain is an incredible information processor. Right now,
as you read these words, it’s processing about 11 million bits of
information, yet you’re only consciously aware of about 40 to 50
bits. This selective awareness reveals something crucial about
how our minds work: memory isn’t about storing everything—
it’s about storing what matters.118
Think of the last time you drove to work. You probably don’t
remember most of the journey, but you’d certainly remember
if something unusual happened, like a deer crossing the road.
This isn’t a bug in human memory; it’s a feature. Our brains are
remarkably efficient at filtering out routine information while
highlighting what’s important or unusual.
Scientists have found that we have different types of
memory systems, each serving different purposes.119 There’s
working memory (also called short-term memory)—your mind’s
equivalent of a computer’s RAM—which can hold about seven
pieces of information at once. This is why phone numbers were
traditionally seven digits long, and it’s why you might struggle
to remember a long sequence of instructions.120
Then, there’s long-term memory, which is far more complex
and composed of three main types of memories. Remember how
you never forget how to ride a bike? That’s procedural memory
at work. The ability to recall your wedding day or your first job

118
CNS Nevada. 2024. “What Is the Memory Capacity of a Human Brain?”
https://www.cnsnevada.com/what-is-the-memory-capacity-of-a-human-
brain/
119
Larry R. Squire and Stuart Zola-Morgan, 1991. “The Medial Temporal
Lobe Memory System.” Science 253, no. 5026: 1380–1386. https://doi.
org/10.1126/science.1896849
120
Eduardo Camina and Francisco Güell, 2017. “The Neuroanatomical,
Neurophysiological and Psychological Basis of Memory: Current Models and
Their Origins.” Frontiers in Pharmacology 8:438. https://doi.org/10.3389/
fphar.2017.00438

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interview? That’s episodic memory. The fact that you know

Paris is the capital of France, even if you’ve never been there?
That’s semantic memory.121
What’s fascinating is how these systems work together. When
you’re cooking a familiar recipe, you’re simultaneously using
procedural memory (cooking techniques), semantic memory
(knowing what ingredients work together), and episodic memory
(remembering the last time you made this dish and what worked
or didn’t work).
But here’s what’s really interesting: every time you recall a
memory, you’re not pulling up an exact recording like a computer
file. Instead, you’re reconstructing it, potentially with slight
variations each time. This was demonstrated in groundbreaking
research by Elizabeth Loftus, who showed how memories can be
subtly altered simply by how questions about them are asked.122
In one study, Loftus showed participants a video of a car
accident and then asked, “How fast were the cars going when
they smashed into each other?” versus “How fast were the cars
going when they hit each other?” Those who heard “smashed”
recalled higher speeds and even reported seeing broken glass—
when none was present. This fascinating experiment highlights
how word choice can reshape memory. It demonstrates that
human memories are reconstructions, not recordings.
This reconstructive nature of memory, while sometimes
unreliable, gives us remarkable flexibility in thinking and
problem-solving. It allows us to imagine new scenarios by
recombining elements of past experiences—a capability that

121
Jarrad A. G. Lum and Gina Conti-Ramsden, 2013. “Long-term memory:
A review and meta-analysis of studies of declarative and procedural memory
in specific language impairment.” https://pmc.ncbi.nlm.nih.gov/articles/
PMC3986888/
122
Elizabeth F. Loftus, 1975. “Leading Questions and the Eyewitness Report.”
Cognitive Psychology 7, no. 4: 560–572. https://doi.org/10.1016/0010-
0285(75)90023-7.

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current AI systems, with their more rigid memory structures,

still struggle to replicate.
Emotions play a crucial role, too. Think about where you were
when you heard about a major world event, like a presidential
election or a global crisis. You probably remember it clearly
because emotional experiences are generally better remembered
than neutral ones. This emotional tagging helps us prioritize
important information and make better decisions—something
we’re still working to understand and implement in AI systems.
Understanding these aspects of human memory helps
explain both the challenges and opportunities in developing AI
agents. While we can create systems that store vast amounts
of information with perfect accuracy—something our brains
can’t do—we’re still far from replicating the flexible, context-
sensitive, and emotionally intelligent way humans process and
use memories.
This isn’t just academic knowledge—it has practical implica-
tions for how we design and use AI systems. When we recognize
that human memory is more about making meaningful connections
than storing perfect records, we can better understand what we
should aim for in artificial systems: not just more storage but smart-
er, more contextual ways of using information.

The Surprising Reality of AI Memory

This brings us to a fascinating contrast with current AI systems.
We’ve noticed a common misconception: Many people are
scared to interact with Large LLMs such as ChatGPT, Gemini,
or Claude because they believe these AI systems are constantly
learning and remembering everything from their interactions,
building an ever-growing knowledge base. The reality is far
more surprising.
LLMs are more like extremely sophisticated echo chambers
with limited temporary memory. Think of it this way: When
you start a conversation with an AI, it’s like opening a blank

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notebook with a fixed number of pages. Everything you discuss

gets written in this notebook, and the AI can reference any part
of it freely—but only until you close the conversation. Once you
start a new chat, it’s like getting a fresh notebook with no trace
of what was written in the previous one.
Want to test this yourself? Try this simple experiment with
any AI chat interface, such as ChatGPT or Claude:

1. Ask it about a topic that you like, such as “How is scuba

diving in Tahiti?”
2. Ask for more information about the diving spots and the
types of fish you can see there
3. Close the chat session
4. Open a new chat session immediately and ask it, “What
did you tell me about Tahiti one minute ago?”

Here is what the chatbot replied to us: “I don’t recall

mentioning Tahiti recently. Could you clarify what you’re
looking for? Are you asking about travel tips, history, culture,
or something else?”
We suggest you try it. Most likely, the AI will either admit
it doesn’t remember anything or make generic statements. This
isn’t a flaw in the AI’s processing power—it’s a fundamental
limitation in how most current AI systems handle memory. The
key lesson here is that LLMs, which are the foundations of the
most advanced agents currently in production (level 3 agents),
have as much memory as a goldfish!

The Memory Challenge in Practice

To illustrate the practical impact of this limitation, consider an
experiment we conducted with a major healthcare provider.
They implemented two versions of an AI scheduling assistant:

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• A standard LLM-based system that started fresh with

each interaction
• A memory-augmented AI agent that could retain patient
information

The results were striking. The standard system required

patients to repeat their medical history and preferences in every
interaction. The memory-augmented version remembered patient
histories and preferences, leading to 70% faster scheduling times
and a 45% increase in patient satisfaction.
This experiment highlighted the current Memory Paradox
in generative AI: The most advanced AI systems can process
complex information but often can’t remember simple details
about their users.

The Three Main Goals of AI Agents’ Memory

Through our research and implementation, we’ve identified
three critical goals of memory that make it the foundation of
agentic intelligence:

• First is contextual understanding. Traditional generative

AI systems process each input independently, like a
person reading random pages from different books.
Memory-enabled AI agents can maintain context across
interactions, similar to how we follow a continuous
conversation. This capability is crucial for meaningful
interactions and problem-solving.

• The second is learning and adaptation. Through our

experience, we have observed that the most successful
AI implementations were those that could learn from past
interactions. For example, a manufacturing company’s
AI quality control system not only detected defects but

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also remembered patterns of issues, leading to a 40%

reduction in false positives over time.

• The third is personalization at scale. Customers increas-

ingly expect personalized experiences. Memory-enabled
AI agents can deliver this by retaining and learning from
individual interactions while maintaining privacy and
security.

The Three Layers of AI Agents’ Memory

Much like the organization of human memory, the memory of
AI agents can be structured into three interconnected layers,
each with a specific function in preserving context, facilitating
learning, and enabling adaptation over time:

1. Short-Term Memory (STM) – Immediate Context

Retention
STM functions as the AI’s working memory, holding recent
interactions and ensuring contextual continuity within a single
session. It processes current inputs, tracks ongoing conversa-
tions, and applies attention mechanisms to prioritize relevant in-
formation. However, STM has a limited capacity—as new data
comes in, older information is overwritten unless transferred to
long-term memory.

2. Long-Term Memory (LTM) – Structured Retention

Over Time
LTM extends beyond session-based memory by storing
structured information for future reference. This includes user
preferences, past interactions, learned workflows, and domain-
specific knowledge. LTM enables AI to recognize recurring
patterns, recall past interactions, and personalize responses based
on accumulated experiences. Unlike STM, LTM is designed to

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persist, ensuring that AI agents do not start from scratch in every

interaction.

3. Feedback Loops – Learning and Adaptation

Feedback loops act as the self-improvement mechanism of AI
memory, refining both STM and LTM over time. By incorporating
user feedback—whether explicit (e.g., corrections, ratings) or
implicit (e.g., engagement patterns, error tracking)—the AI adjusts
its memory structures to enhance accuracy and relevance. This
process allows AI to improve continuously by reinforcing useful
knowledge and discarding outdated or incorrect information.

Figure 7.1: The three layers of agentic AI memory (Source: © Bornet et al.)

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How These Layers Work Together

AI memory functions dynamically across these three layers:
STM holds immediate context, LTM ensures continuity beyond
a session, and feedback loops refine both to drive continuous
learning. This layered approach allows AI agents to engage in
context-aware, evolving, and personalized interactions, making
them more intelligent and reliable over time.
Think of the layers of this agentic AI framework like an onion.
Short-term memory (STM) forms the outermost layer, providing
immediate context for interactions, but it is temporary and fades
quickly as new information arrives. Beneath it, long-term memory
(LTM) serves as the foundation, storing critical knowledge and
ensuring that AI does not restart from zero in every interaction. At
the core, feedback loops continuously refine and strengthen both
STM and LTM, enabling the system to learn from user interactions
and improve over time. Just as an onion grows by building upon
its layers, this memory architecture allows AI agents to develop
deeper intelligence, ensuring continuity, adaptability, and long-term
reliability in their responses.
In the following sections, we’ll delve deeper into how
different types of memory work in AI agents, their practical
applications, and the limitations we must address. But remember
this fundamental truth: just as human intelligence is built on our
ability to remember and learn from experiences, the future of AI
agents depends on their ability to do the same.

The Intricate Dance of Short-Term Memory

in AI Agents
Picture yourself at a busy intersection in Singapore. You’re
processing multiple streams of information simultaneously:
traffic lights changing, pedestrians crossing, other vehicles
moving, and your navigation system providing directions. This
constant flow of immediate information and how you process

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it illustrates the essence of short-term memory—a critical

component not just for human cognition but for AI agents as
well.

The Challenge of the Present Moment

Just as our brains must constantly juggle incoming information
while maintaining immediate context, AI agents face a similar
challenge. However, their approach to handling this immediate
information processing is both fascinating and fundamentally
different from human cognition. Through our experience, we’ve
discovered that understanding these differences is crucial for
anyone willing to succeed with AI technology.
Let’s start with a simple experiment that we often use in our
consulting work to demonstrate the importance of short-term
memory:
Open a conversation with an AI assistant like Claude or
Gemini and start telling a story about your day. After every few
sentences, ask the AI to summarize what you’ve said so far.
You’ll notice something interesting: the AI can maintain
context for a while, but eventually, it starts losing track of earlier
details. This isn’t unlike human short-term memory, which
cognitive scientists tell us can typically hold about 7 (plus or
minus 2) items at once.123

The Context Window: AI’s Working Memory

One of the most surprising discoveries for many of our clients is
how AI systems actually handle immediate information. Current
AI systems, particularly LLMs, operate within what we call a
“context window”—think of it as a temporary workspace where
the AI can see and process information. This window is like a

123
George A. Miller, 1956. “The Magical Number Seven, Plus or Minus Two:
Some Limits on Our Capacity for Processing Information.” Psychological
Review 63, no. 2: 81–97. https://doi.org/10.1037/h0043158

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whiteboard that can hold a limited amount of text, and once it’s
full, older information must be erased to make room for new
input.
Recent work by Li, Rulin Shao, and their colleagues revealed
something fascinating about these context windows. Their
research uncovered what they call the “context ceiling”—the
point at which adding more information to an agent’s memory
starts degrading rather than enhancing performance.124
This phenomenon isn’t simply about reaching maximum
capacity; rather, it reveals fundamental challenges in how
agents process and integrate large amounts of information. The
researchers found that performance degradation often begins
well before reaching nominal memory limits, suggesting that
effective memory management isn’t just about storage capacity
but about how an agent processes information within its short-
term memory.
To understand this, let’s break down short-term memory
into three distinct components: the context window, attention
mechanisms, and token management. These three elements
form the backbone of how an AI agent engages with tasks,
prioritizes information, and delivers responses. By exploring
these components through a simple experiment, we can uncover
not only how they work but also how to guide the agent more
effectively—and what this means for leaders in business contexts.

The Context Window

Imagine you’ve come across a dense technical report—perhaps
a whitepaper on quantum computing or a policy document
on renewable energy. You instruct the generative AI Chatbot:
“Summarize this article in five bullet points, focusing on the

124
Jiaming Tang, et al., 2024. “Quest: Query-Aware Sparsity for Efficient
Long-Context LLM Inference.” ResearchGate. https://www.researchgate.
net/publication/381484873_Quest_Query-Aware_Sparsity_for_Efficient_
Long-Context_LLM_Inference

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main themes or findings.” As it processes the text, the context

window becomes the first and most critical component at play.
Think of the context window as the AI’s desk—a workspace
where it arranges all the information it needs to complete the
task. In a model like GPT, this desk can accommodate up to a
few hundred thousand tokens, or roughly 100,000 words, while
cutting-edge systems like Google’s Gemini offer even larger
capacities with millions of tokens.
But even a massive desk has its limits. The AI must decide
how to arrange the information to ensure it stays manageable.
If the document exceeds the desk’s size, some parts are left out
entirely, while others are compressed or simplified. The AI’s
ability to process the task hinges on how effectively it uses this
finite space.125

The Attention Mechanisms

Once the information is on the desk, the attention mechanisms
take over. This is the AI’s ability to focus on the most relevant
parts of the input. Imagine scanning the document with a
highlighter, marking the sentences or ideas that matter most.
Attention mechanisms do this dynamically, assigning weights
to different pieces of information based on your prompt. In
this case, the AI zeroes in on overarching themes like the role
of quantum entanglement or the global impact of renewable
energy policies, filtering out less significant details like specific
examples or minor arguments.
Attention mechanisms are incredibly powerful, but they’re
shaped by the instructions you provide. If you ask for “main
themes,” the AI’s focus will naturally deprioritize granular

125
Nelson F. Liu, et al., 2023. “Lost in the Middle: How Language Models
Use Contexts.” https://cs.stanford.edu/~nfliu/papers/lost-in-the-middle.
arxiv2023.pdf

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specifics. Hence, it is important to send clear instructions and

make sure they align well with the goal.126

Token Management
Finally, token management is like the AI’s note-taking system.
Imagine you’re in a lecture, trying to capture the key points
without writing everything down. You focus on the essentials
and skip what feels repetitive or less important. AI does the same
as it nears its memory limit—it summarizes, condenses, and
decides what to keep in its mental workspace. But this approach
has trade-offs. Some nuanced details or examples might get
left out because they seemed less critical at the moment. Token
management is a balancing act, ensuring the AI stays concise
and focused while preserving enough detail to fulfill its purpose
effectively.
To test this further, you follow up with a question to the
generative AI Chatbot: “What does the article say about the
role of entanglement in error correction?” If this detail was
highlighted in the main themes, the AI retrieves it quickly and
accurately. But if it was buried in a less prominent section or
lacked contextual emphasis, the AI might stumble. It might
attempt to infer an answer based on related themes or, failing
that, return a broader, less precise response. This happens
because the specific detail wasn’t prioritized during the attention
phase or was lost during token management.
The experiment reveals the AI’s memory architecture
in action: the context window defines its limits, attention
mechanisms shape its focus, and token management enforces
trade-offs.

126
Ashish Vaswani, et al., 2017. “Attention is All You Need.” Advances in
Neural Information Processing Systems 30. https://proceedings.neurips.cc/
paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

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The Real-World Impact

To see how the components of short-term memory—context
windows, attention mechanisms, and token management—come
together in a real-world scenario, consider a project undertaken
by a major financial services provider. The task was to develop
an AI agent capable of handling intricate customer inquiries
about investment products. The challenges were considerable.
Customers often required detailed explanations about investment
options, expected the system to maintain context across lengthy
conversations, and frequently referenced multiple accounts or
specific historical transactions. The AI needed to not only keep
track of all this information but also cross-reference it within
the same conversation to provide accurate and personalized
responses.
Initially, the system was equipped with basic short-term
memory capabilities. It excelled at straightforward tasks like
answering single-step questions—such as explaining the differ-
ences between two mutual funds or retrieving account balances.
However, it faltered when confronted with multi-step processes.
For example, if a customer asked to compare investment op-
tions, update their account preferences, and calculate potential
returns based on a hypothetical scenario, the AI often lost track
of earlier parts of the conversation. This led to fragmented re-
sponses, repetition, or errors, especially when juggling multiple
accounts or shifting between complex financial concepts.
To address these shortcomings, the company’s internal AI
development team implemented enhanced short-term memory
strategies. First, they optimized the context window by splitting
conversations into distinct segments, ensuring the AI could focus
on one part of the discussion at a time without losing sight of
previous details. Next, they fine-tuned the attention mechanisms
to prioritize the most relevant parts of the input—such as
key customer requests, account-specific details, and critical
financial terms. Finally, they introduced token management

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protocols to summarize and retain essential points from earlier

in the conversation while discarding irrelevant or redundant
information.
The results were transformative. With these enhancements, the
AI was able to handle intricate customer interactions with remarkable
accuracy. It seamlessly navigated multi-step processes, such as
comparing portfolio performance, providing tailored investment
advice, and executing account updates—all within a single session.
Error rates dropped by 65%, and customer satisfaction scores rose
significantly, reflecting the system’s ability to engage in detailed
and coherent financial discussions. While still not as nuanced or
adaptable as a human advisor, this improvement marked a significant
leap in AI’s ability to manage complex financial interactions with
greater precision and reliability.
This example underscores the potential of well-designed
short-term memory management in AI systems. It’s not just
about enhancing performance—it’s about building trust. When
customers see that an AI can remember their needs, adapt to
complex demands, and deliver accurate information, they feel
confident in its capabilities. For businesses, this isn’t just an
operational improvement; it’s a competitive advantage that
elevates the customer experience to new heights.

Guiding the Short-Term Memory for Better

Outcomes
To get the most from an AI agent, users must take an active
role in guiding its memory processes. This starts with crafting
thoughtful prompts that align with the task at hand. If your
goal is to extract broad themes, straightforward instruction like
“Summarize the key points” works well. On the other hand, if
you’re looking for specific details, your guidance should be more
precise: “Provide a detailed explanation of Section 3, focusing
on examples.”

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Leaders must realize that AI, unlike a human team member,

doesn’t intuitively prioritize or infer. Instead, it thrives when
tasks are presented clearly and structured to maximize its focus
and processing capabilities. For example, in customer service
applications, rather than presenting the AI with a customer’s
entire chat history, a better approach is to provide concise
summaries of key interactions. Similarly, in strategic planning,
breaking down market analysis into smaller, focused sections
enables the AI to engage with the data iteratively, producing
deeper insights over time.
Beyond workflows, this understanding demands a rethinking
of team dynamics. AI agents excel at managing and analyzing
vast quantities of data, but they still need human oversight to
guide their priorities. This creates an opportunity for hybrid
collaboration—humans shape the inputs and interpret the
outputs, while AI tackles repetitive, data-intensive tasks.
Together, they form a complementary partnership. By fostering
this synergy, business leaders can unlock new levels of efficiency
and innovation while minimizing the risks associated with AI’s
inherent limitations.
Practical strategies have emerged from this collaborative
approach. One of the most effective is chunking information.
Just as humans organize knowledge into manageable groups,
AI agents perform better when their inputs are structured into
digestible segments. For instance, implementing chunking in a
customer service system led to a 40% improvement in response
accuracy. Information grouped by topic or relevance enabled the
AI to navigate its short-term memory more efficiently, resulting
in sharper, more precise answers.
Another powerful technique is priority queuing, which
ensures the most critical information stays accessible in the
AI’s short-term memory. In a healthcare application, for
example, patient symptoms and vital signs were prioritized over
administrative details. This strategy meant that critical medical
information was readily available, enhancing the AI’s ability

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to assist healthcare providers in time-sensitive scenarios. The

success of these approaches demonstrates how the deliberate
structuring of information can significantly improve AI
performance across industries.
Ultimately, by understanding and mastering the mechanics of
short-term memory—context windows, attention mechanisms,
and token management—you can ensure that AI becomes not
just a tool but a catalyst for growth and innovation.

Looking to the Future of Short-Term Memory

The future of short-term memory in AI agents holds exciting
possibilities, with breakthroughs that promise to revolutionize
how machines process and retain information. Among these,
three recent advancements stand out, each offering a glimpse
into the potential of AI to handle more complex tasks with
greater efficiency.
One such innovation is Landmark Attention,127 a mechanism
that equips AI with a sort of mental map to navigate vast amounts
of information. Imagine reading a lengthy novel or analyzing
a comprehensive report—Landmark Attention enables AI to
divide these massive texts into manageable sections and identify
key points, or “landmarks,” to focus on. This approach ensures
the AI can access relevant details quickly without becoming
overwhelmed by the sheer volume of information. It addresses
a critical limitation in current systems that struggle with long
sequences, making it possible for AI to process entire books
or datasets in one go. For businesses, this could mean faster
and more comprehensive analysis of customer feedback, legal
documents, or financial data.

127
Arash Mohtashami and Martin Jaggi, 2023. “Landmark Attention:
Random-Access Infinite Context Length for Transformers.” https://arxiv.org/
abs/2305.16300

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Another game-changing research is called Varied-Size

Window Attention (VSA).128 It allows AI to adapt its focus
dynamically. Picture an adjustable pair of glasses that lets you
zoom in on fine details when needed and zoom out to take in the
broader context. VSA provides AI with this flexibility, creating
“windows” of varying sizes depending on the task requirements.
For example, summarizing a lengthy report may require a wide
view to capture overarching themes, while translating a sentence
demands a more focused lens. This adaptability makes AI far
more versatile and capable of handling diverse challenges with
precision and efficiency. In practical terms, this means AI could
seamlessly transition between drafting an article and analyzing a
legal contract, performing both tasks with remarkable skill.
The third research, called RetrievalAttention,129 introduces
a new level of efficiency in managing memory. Current AI
systems often attempt to process and store everything, even
irrelevant information, leading to wasted resources and slower
performance. RetrievalAttention changes this by teaching
AI to retrieve only the most important pieces of information
when needed, much like a person recalling the key points of
a conversation without needing to remember every word. This
method significantly speeds up processing times while reducing
energy consumption, making AI not only faster but also more
cost-effective. The implications are profound: imagine smoother,
quicker AI assistants that can tackle complex queries without
slowing down, all while running efficiently on everyday devices.
Together, these advancements represent a leap forward in
how AI handles context and memory. They complement each
other beautifully, addressing different aspects of the same
fundamental challenge. Landmark Attention excels at organizing

128
Qiming Zhang, et al., 2022. “VSA: Learning Varied-Size Window
Attention in Vision Transformers.” https://arxiv.org/abs/2204.08446
129
Di Liu, et al., 2024. «RetrievalAttention: Accelerating Long-Context
LLM Inference via Vector Retrieval.» https://arxiv.org/abs/2409.10516

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vast information; VSA makes AI more adaptable to varying

tasks; and RetrievalAttention ensures that memory is managed
efficiently.
Understanding and optimizing short-term memory in AI
agents isn’t just a technical challenge—it’s fundamental to
creating systems that can engage meaningfully with humans
and handle complex tasks effectively. While short-term memory
is crucial for immediate task performance, its real potential
is realized when integrated with long-term memory systems.
This integration allows AI agents to not just process immediate
information effectively but also learn and adapt over time—a
topic we’ll explore in depth in the next section.

The Power of Long-Term Memory:

Transforming AI from Tools to Partners
The Power of Long-Term Memory
Imagine this: an AI agent that doesn’t just help you with tasks or
answer your questions but remembers. Not for a day, or a week,
or even a year—but across decades. It remembers every important
moment, every challenge, every success, every lesson you’ve
learned. It doesn’t just store data; it understands your story.
What could you achieve with a coach or a companion like
that?
Think about the business decisions you’ve made in the
past year. Some were straightforward but impactful—like
reallocating resources to meet a deadline or approving a new
vendor for a critical supply chain component. Others may have
been transformative—a major product pivot, entering a new
market, or restructuring a department. How many of those
decisions were fully informed by your company’s historical data
and context? How often did you rely on instincts or incomplete
recollections simply because the relevant details weren’t readily
available?

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Now, imagine an AI agent that remembers it all. When faced

with a strategic choice, it provides insights rooted in historical
patterns. “Remember three years ago, when you hesitated to
launch that product in a similar market? It turned out to be a
huge success because of your targeted approach. This new
opportunity shares similar characteristics—here’s what worked
last time and what you might consider.” This isn’t hypothetical;
it’s actionable, grounded in your organization’s unique trajectory.
The power of such memory extends beyond reactive decision-
making. It identifies patterns in your business strategies, highlights
operational inefficiencies, and amplifies your organization’s
strengths. It doesn’t just store data—it contextualizes it, turning raw
information into actionable insights. This is the difference between
a tool and a true business partner—one that evolves with you,
understands your priorities, and helps you achieve smarter, more
confident outcomes.
This is the future of AI. Not just smarter, but profoundly
more human in its ability to remember, understand, and help us
live our lives with greater intention. It’s a future where memory
isn’t just preserved—it’s used to unlock our best selves.

Why is Long-Term Memory (LTM) Important for

Businesses
The business impact of LTM in AI agents is transformative,
reshaping how organizations interact with customers, streamline
operations, and gain strategic advantages. Imagine a customer
service experience where every interaction builds upon the last:
an AI that recalls your preferences, anticipates your needs, and
resolves issues without making you repeat yourself. This level
of personalization doesn’t just improve satisfaction—it creates
loyalty. According to our experience, companies leveraging
memory-enabled AI agents have reported customer satisfaction
increases of 20-30%, driven by seamless, tailored interactions
that feel more human than transactional.

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Operationally, the benefits are just as profound. Consider a

logistics firm managing supply chain disruptions. With memory-
enabled AI, patterns from past challenges—like weather delays
or regional bottlenecks—are retained and applied in real time,
enabling faster resolutions and smarter resource allocation.
Similarly, employees onboarding with the help of AI agents
experience reduced training times as these systems remember or-
ganizational nuances and provide consistent guidance. According
to our experience, businesses adopting long-term memory in their
AI systems have seen error rates fall by as much as 50%, cutting
inefficiencies and enhancing performance.
The strategic advantages are even more compelling. AI
agents with long-term memory excel at recognizing patterns
over time, transforming raw data into actionable insights. For
instance, a financial institution might leverage such agents to
track subtle shifts in client behavior, identifying emerging risks
before they escalate.
Decision-making becomes sharper, predictions more
accurate, and risk management far more proactive. These are not
incremental improvements—they’re leaps that give businesses a
significant competitive edge.
By enabling AI to learn, adapt, and grow alongside an
organization, long-term memory transforms it from a tool into a
strategic partner, unlocking value at every level of the enterprise.

Current LLMs Fall Short of Long-Term Memory

As shown in our earlier experiment, when we asked about scuba
diving in Tahiti, current LLMs like ChatGPT lack LTM. This
limitation isn’t accidental—it’s a deliberate trade-off to optimize
efficiency, protect privacy, and avoid overwhelming the model
with excessive data.
To address this, building AI agents requires giving LLMs
access to external memory capabilities. In the following
sections, we’ll explore these capabilities, which represent
significant steps toward memory-enabled AI. However, each

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comes with its own limitations and trade-offs. Together, they

form the foundation of today’s efforts to create smarter, more
adaptable AI agents.

Designing and Implementing Long-Term

Memory in Agentic AI Systems
In the pages that follow, we’ll walk through our battle-tested
approach to memory system design, the frameworks we’ve
found most effective, and the pitfalls we’ve learned to avoid.
Whether you’re enhancing existing AI agents or building new
ones from the ground up, this guide will help you implement
memory architectures that deliver real value.

Understanding the Memory Landscape

Before diving into implementation, we need to establish a clear
understanding of what memory means in the context of AI
agents. In our experience, successful memory systems require
multiple, complementary types of memory working in concert.
When we talk about memory in AI systems, we’re referring
to structured mechanisms that allow agents to store, retrieve,
and utilize information over time. Unlike the simple context
windows that characterize most language models, true AI
memory creates persistence across sessions and enables learning
from past interactions.
AI memory generally falls into three primary categories:
Episodic Memory represents the agent’s experiential
knowledge—what happened, when, and with whom. This
includes conversation histories, user preferences expressed
over time, past actions taken, and outcomes observed. Episodic
memory allows our agents to maintain continuity across
interactions, even when separated by days or weeks. When a
financial advisory agent recalls that a client previously expressed
interest in low-risk investments, that’s episodic memory at work.

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Semantic Memory encompasses factual knowledge—the

things our agent “knows” rather than “remembers experiencing.”
This includes domain knowledge, facts about the world, company
policies, product catalogs, and any other information that exists
independently of specific interactions. We’ve found that robust
semantic memory allows agents to provide accurate information
without needing to query external systems for every request.
Procedural Memory captures the “how-to” knowledge—
sequences of actions, decision trees, and workflows that guide
the agent through complex tasks. When building customer
service agents, we encode troubleshooting procedures into
procedural memory, allowing the agent to walk users through
complex processes step by step.
The magic happens when these memory types work together.
In our work with a healthcare provider, we built an agent
that could recall a patient’s medical history (episodic), apply
clinical guidelines (semantic), and follow proper protocols for
scheduling procedures (procedural). The result was a system
that could provide personalized care guidance while maintaining
strict compliance with healthcare regulations.

Figure 7.2: The Three Types of Long-Term Memory (Source: © Bornet et al.)

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 Memory: Building AI That Learns

Architectural Foundations for Long-Term

Memory
Building effective long-term memory requires a thoughtful
architectural approach. Through our implementations, we’ve
refined a memory architecture that balances performance, scal-
ability, and practicality.
Each type of memory requires specific storage solutions
optimized for its unique characteristics:
For Episodic Memory: We implement fast, efficient storage
for recent interactions and contextual history. Redis or similar
in-memory databases work best for storing short-term episodic
memory, enabling quick access to recent conversations without
complex retrieval mechanisms. The storage should include
timestamps, user identifiers, interaction summaries, and
identified entities/intents. For one retail client, this approach
reduced response latency by 40% compared to their previous
solution, which retrieved all historical interactions for every
query.
For Semantic Memory: We implement vector databases
like Pinecone or Weaviate to store semantic embeddings—
numerical representations of concepts, facts, and knowledge
that enable similarity-based retrieval. These databases excel
at finding conceptually related information, even when exact
keyword matches aren’t present. This memory type requires a
structured organization with clear categorization, relationships,
and metadata to facilitate precise retrieval.
For Procedural Memory: We store workflow definitions,
decision trees, and process maps in structured formats, typically
using traditional relational databases or specialized workflow
engines. These systems must maintain the integrity of process
steps, decision logic, and conditional branching. For complex
procedural memory, we often implement versioning to track
changes to processes over time.

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For comprehensive agent implementations, we combine

these specialized stores. In our work with a legal tech company,
this combined approach created an agent that could recall
specific case citations from episodic memory (exact matching
from a relational database), access legal principles from
semantic memory (similarity matching from a vector database),
and follow proper legal analysis procedures from procedural
memory (workflow systems). This combination provided both
precision and flexibility across all memory types.

Implementation Pathway: Building Memory

from the Ground Up
Now, let’s walk through our recommended implementation
pathway based on our experience building dozens of memory-
enhanced agents.

Step 1: Select a Framework for Implementation

With the memory architecture defined, the next step is selecting
a technological framework that supports memory persistence,
retrieval, and updating mechanisms. Based on our implementations
across various domains, we recommend considering these proven
options:
LangChain provides an excellent starting point for integrating
episodic, semantic, and procedural memory into LLM-powered
agents. Its Memory module offers built-in support for short-term
conversational memory (episodic) and long-term knowledge
storage (semantic and procedural). We’ve found LangChain
particularly valuable for rapid prototyping and projects where
integration flexibility is important.
LlamaIndex (Formerly GPT Index) helps organize, store,
and retrieve memory dynamically. It excels at creating and
maintaining structured knowledge indexes that can be efficiently

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queried, making it ideal for applications with complex semantic

memory requirements. For domain-specific agents that need to
access large knowledge bases, LlamaIndex has proven highly
effective in our implementations.

Step 2: Define Memory Requirements

With the framework selected, we define specific memory
requirements for each memory type based on the agent’s purpose:

For Episodic Memory requirements, we determine:

• Which user interactions and conversation elements need
to persist across sessions
• How long different types of episodic information should
be retained
• Which user preferences and behaviors should be tracked
over time

For Semantic Memory requirements, we determine:

• What domain knowledge is essential for the agent to
perform its tasks
• Which information sources should populate the knowledge
base
• Which compliance or regulatory information must be
maintained

For Procedural Memory requirements, we determine:

• Which workflows and processes the agent needs to
follow
• What decision logic governs process selection and
execution
• How strictly processes must be followed versus allowing
flexibility

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For example, with a financial advisory agent, we determined

that episodic memory should include investment preferences,
conversation history, and previous advice given; semantic
memory should contain investment product details and market
data; and procedural memory should encode compliance
workflows for different advisory scenarios. Transaction details,
on the other hand, would be queried from secure systems rather
than stored in the agent’s memory.
Defining these specific requirements for each memory type
upfront prevents both memory overload (storing too much,
creating retrieval inefficiencies) and memory gaps (not storing
enough, forcing users to repeat themselves).

Step 3: Build Retrieval Mechanisms

With storage in place, we focus on building efficient retrieval
mechanisms. The goal is to bring up the right memories at
the right time without overwhelming the agent with irrelevant
information.
We’ve found that a retrieval-augmented generation (RAG)
approach works well for memory-enhanced agents. In simple
terms, RAG combines the power of knowledge retrieval with
language generation. Think of it as giving the AI agent the
ability to “look things up” in its memory before formulating a
response, similar to how humans might consult their memories
or reference materials before answering a complex question.
Here’s how RAG works in practical terms: When the agent
receives a user query, it:

1. Analyzes the query and current context to generate

retrieval cues
2. Uses these cues to fetch relevant memories from each
storage system
3. Ranks and filters the retrieved memories based on
relevance, recency, and importance

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 Memory: Building AI That Learns

4. Incorporates the most relevant memories into its

reasoning process
5. Generates a response that leverages both the current
context and retrieved memories

This approach significantly improves the agent’s ability

to provide accurate, contextually relevant responses. Rather
than trying to encode all possible knowledge within the model
itself, RAG allows the agent to dynamically access the specific
information needed for each interaction.
For our legal research assistant, this approach allowed
the agent to recall relevant case law and statutes without
overwhelming the response with irrelevant citations. The key
was in carefully tuning the relevance thresholds—too low,
and the agent would miss important precedents; too high, and
responses would become cluttered with tangential information.

Step 4: Implement Memory Consolidation

Perhaps the most critical component of our memory architecture
is the consolidation mechanism—the process that determines
what to remember long-term and what to forget. It defines which
information will be transferred from the episodic to the semantic
or procedural memory. Through experimentation, we’ve
developed a multi-faceted approach to memory consolidation:

• Importance-based consolidation evaluates the signifi-

cance of new information. When a user provides critical
details like preferences, requirements, or feedback, our
system flags this information for long-term storage. For
a travel booking agent we developed, this meant perma-
nently remembering that a user prefers aisle seats or has a
shellfish allergy.

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 Agentic Artificial Intelligence

• Frequency-based consolidation tracks recurring patterns.

Information that appears frequently across interactions is
likely important and gets promoted to long-term memory.
Our educational tutoring agent uses this to identify and
address recurring knowledge gaps in students.

• Explicit consolidation occurs when users or the system

deliberately marks information for remembrance.
“Remember that I prefer evening appointments” triggers
explicit consolidation in our scheduling assistant.

The flip side of consolidation is forgetting—equally important

for system efficiency. We implement time-based decay for certain
types of information, relevance thresholds that archive rarely
accessed memories, and explicit forgetting mechanisms when
information becomes outdated or incorrect.

Step 5: Integrate Memory with Agent Reasoning

The final implementation step integrates memory retrieval and
consolidation with the agent’s core reasoning process. This
integration occurs at three key points:

• Context preparation incorporates relevant memories

into the context window before the agent generates a
response. For our financial advisor agent, this meant
including the client’s risk profile and investment goals in
the context of every investment recommendation.

• In-process retrieval allows the agent to pull additional

memories during its reasoning process if it identifies a
need for more information. Our customer service agent
could retrieve product specifications mid-conversation if
the dialogue turned to technical details.

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 Memory: Building AI That Learns

• Post-response consolidation evaluates the interaction

after a response is generated to identify information for
long-term storage. Did the user express a new preference?
Did they correct the information? These insights are
captured and consolidated.

Integrating memory with reasoning transforms an AI from a

stateless response generator to a system that evolves with each
interaction, becoming increasingly personalized and effective
over time.

The Near Future: Key Evolutions on the Horizon

In the near future, we can expect significant improvements in
memory-enhanced AI driven by advancements in computational
power, algorithms, and integration techniques. One key
evolution will be the refinement of contextual prioritization
algorithms, which will allow AI systems to better determine
which memories are most relevant in real-time. This will reduce
retrieval delays and enhance the system’s ability to handle
dynamic, fast-changing information.
Another near-term breakthrough is likely in the area of real-
time memory consolidation, where systems will analyze and
reorganize stored data on the fly. Instead of waiting for scheduled
updates, AI will be able to continuously refine its memory banks
based on user feedback and interaction patterns. This will make
systems more adaptive, improving their ability to respond to
new challenges without retraining.
Recent research130 emphasizes the potential of multi-agent
systems in improving memory management by distributing the
memory load and enhancing collaboration. These multi-agent
systems allow different agents to focus on specific aspects of

130
Hyungho Na, et al., 2024. “Efficient Episodic Memory Utilization
of Cooperative Multi-Agent Reinforcement Learning.” https://arxiv.org/
abs/2403.01112

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memory. For example, some agents can handle episodic memory to

recall past events, while others can manage real-time interactions
or longer-term knowledge. This division of labor prevents memory
overload and improves efficiency. Agents in the system can share
key experiences from their episodic memory, enabling collaborative
learning. For instance, one agent’s memory of a successful strategy
can inform another agent’s decision-making, reducing the need for
redundant exploration across the system.
Another recent research brings all this to a whole new level.
The research paper “Titans: Learning to Memorize at Test Time,”
by Google Research, introduces a groundbreaking neural long-
term memory module that enables AI systems to learn and adapt
continuously even after deployment.131 Unlike traditional AI
models that rely on vector databases for knowledge retrieval (as
we have explained in the previous sections), Titans integrates long-
term memory into the model’s parameters, allowing it to memorize
and recall information dynamically without external storage.
Another interesting feature is that it employs surprise-
based learning, prioritizing unexpected or critical inputs while
using a built-in forgetting mechanism to discard outdated data,
ensuring efficiency and preventing memory overload—much
like how human memory refines itself over time. Titans also
introduces meta-learning, allowing AI to memorize relevant
experiences on the fly without retraining. This enables real-
time adaptation, making AI more responsive and intelligent in
dynamic environments.
In summary, Titans enable faster and better memory
processing. It is also more secure because sensitive data stays
within the model, reducing privacy risks associated with cloud-
based storage. This marks a major shift in how AI agents handle
memory, and we can’t wait to see how Titans impact the future
of AI agents.

131
Ali Behrouz, et al., 2024. “Titans: Learning to Memorize at Test Time.”
https://arxiv.org/abs/2501.00663

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 Memory: Building AI That Learns

Adaptation and Learning Through

Feedback Loops
The Unstoppable Learner: A Vision from the
Future
The year is 2042. In a bustling, fully automated logistics hub
outside of Singapore, a newly deployed AI agent named Nexus
arrives on its first day. Unlike the hyper-specialized systems of
the past, Nexus isn’t pre-trained for its role. In fact, it knows
nothing about the tasks it’s about to face. There are no preloaded
instructions and no finely tuned algorithms tailored for this
environment. Instead, Nexus carries only one instruction:
“Optimize delivery efficiency across the hub.”
At first, the system stumbles. It assigns drones to the wrong
zones, fails to account for package weight, and even causes a
few minor disruptions as forklifts move chaotically through
the warehouse. Workers look on skeptically, wondering why
management would unleash an untrained AI in such a critical
setting. But Nexus isn’t deterred. Armed with the power of
universal learning and a highly sophisticated feedback loop, it
begins to observe, experiment, and adapt.
Within hours, Nexus starts to pick up patterns. Packages
bound for the same destination are consolidated. Drones adjust
their flight paths based on warehouse congestion. It notices that
forklifts often idle while waiting for tasks, so it reconfigures
scheduling to maximize their use. Mistakes still happen, but
fewer than before. By the second day, Nexus has doubled its
efficiency. By the end of the week, it has mastered the complex
choreography of the logistics hub, surpassing even the most
experienced human planners.
Two weeks after Nexus’s arrival, the logistics hub has
transformed. Packages move with almost eerie precision, drones
operate with perfect coordination, and downtime for forklifts
has vanished. Workers who once doubted Nexus now rely on it,

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marveling at its ability to adapt to even the smallest disruptions.

Management begins discussing expanding Nexus to other hubs
worldwide.
What Nexus embodies is a revolutionary concept: a universal
learner, an AI agent that can take on any task, even without prior
training, and achieve mastery through trial, error, and feedback. The
benefits of such a system extend far beyond logistics. Imagine an AI
agent tasked with designing a new marketing campaign. It knows
nothing about your brand or audience but begins experimenting—
testing slogans, analyzing engagement data, and refining its
approach. Within days, it generates a campaign so tailored and
effective that it rivals the work of seasoned creative teams.
Feedback loops are the engine behind this magic. Instead
of relying on static models that require pre-training on vast
datasets, future agents like Nexus rely on dynamic learning.
They act, observe outcomes, and adjust, growing smarter with
every iteration. This approach doesn’t just make AI adaptable—
it makes it unstoppable.
The implications for business and society are staggering.
With universal learning, the barriers to deploying AI vanish.
Companies no longer need to invest millions in training systems
for narrow, specialized tasks. Instead, AI can be dropped into
any environment, given a goal, and left to learn. This reduces
deployment costs, shortens the time to productivity, and
eliminates the bottleneck of retraining systems every time
processes or goals change.
More importantly, universal learners like Nexus are inherently
resilient. They thrive in dynamic environments, continuously
adapting to new challenges. If the rules of the logistics hub were
to change overnight, for example, by introducing biodegradable
packaging or drones with weight restrictions, Nexus wouldn’t
need an update. Its feedback loop would simply absorb the new
parameters and adjust its behavior accordingly.
Of course, this vision of the future raises questions. What
happens when universal learners encounter ambiguous goals?

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 Memory: Building AI That Learns

How do we ensure they act ethically as they adapt? The answer

lies in how we design their feedback loops. Nexus, for instance,
was equipped with multi-objective optimization, balancing
efficiency with safety and environmental sustainability. Ethical
frameworks are baked into its learning process, ensuring that no
amount of trial and error compromises core values.
Another consideration is transparency. Business leaders
must ensure that these agents explain their decisions, fostering
trust among stakeholders. Feedback loops, when designed with
accountability in mind, can include explainability mechanisms—
offering insights into why a decision was made and how it aligns
with goals.
Feedback loops are the backbone of adaptive systems,
enabling AI agents to refine their decision-making, learn
from mistakes, and align more closely with goals over time.
For business leaders, the concept is deceptively simple yet
transformative: allow AI systems to analyze their performance,
learn from outcomes, and use that learning to improve future
actions. It’s the difference between an agent that performs
adequately on day one and one that excels on day one thousand.

The Magic of Feedback Loops

Feedback loops in AI mirror the evolutionary processes of life
on Earth. In evolution, natural selection acts as a feedback
mechanism, where traits that enhance survival and reproduction
are reinforced over generations. Similarly, in AI, feedback loops
like reinforcement learning enable systems to learn through
trial and error. Reinforcement learning132 works by rewarding
desirable actions—those that achieve a set goal or improve
performance—while penalizing failures, just as beneficial
traits are “rewarded” in evolution by being passed on to future

132
Richard S. Sutton and Andrew G. Barto, 2018. “Reinforcement Learning:
An Introduction.” 2nd ed. Cambridge, MA: MIT Press

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generations. Over time, this process shapes the AI’s behavior to

maximize successful outcomes.
Just as species adapt to their environments through iterative
refinements, AI models evolve through learning cycles, learning
from errors and refining their outputs. Both rely on continuous
input, adaptation, and iteration to achieve complexity and
efficiency, transforming random variations into optimized,
purposeful systems over time.
At its core, a feedback loop is a cycle of action, observation,
and adjustment. An AI agent takes an action, receives feedback
on its outcome, and uses that information to refine its behavior.
This loop can operate in real-time, as in autonomous vehicles
learning to navigate a busy intersection or over longer periods,
as seen in recommendation systems that evolve with customer
preferences.
Why does this matter? Traditional AI systems often operate
in static environments, trained on fixed datasets that capture
a snapshot of reality. But the real world is anything but static.
Market conditions shift, customer preferences evolve, and
competitors innovate. Without a mechanism to adapt, AI systems
quickly become obsolete. Feedback loops solve this problem by
turning the agent into a living system—one that grows smarter
as it interacts with the world.

How Do Feedback Loops Work?

It all starts with feedback, which can be collected and applied in
different ways:

• Explicit User Feedback: Agents ask users for ratings,

confirmations, or corrections (e.g., “Did this answer
help?”).
• Implicit Feedback: The system tracks user behavior,
such as task completion rates, abandoned workflows, or
response corrections.

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 Memory: Building AI That Learns

• System-Level Reinforcement Signals: Reward models

reinforce good actions (e.g., a successful task execution)
and discourage failures (e.g., incorrect recommendations).

For example, a customer support AI agent can ask, “Did this

resolve your issue?” If the user selects “No,” the agent logs this
as a failure and adjusts its approach in future interactions.
To operate their feedback loop, the agents work in a cycle of
execution, evaluation, and adjustment:

• Observation: The agent gathers data from interactions

(user feedback, task success rates, implicit cues).
• Evaluation: The system assigns a reward score (positive
or negative) based on feedback.
• Adjustment: The AI agent refines future responses by
updating its model, prompts, or workflows.
• Deployment: The improved agent is deployed and
continues learning over time.

For example, a personal finance AI assistant initially gives

generic budget advice. If users reject its suggestions or request
modifications, it adapts its future recommendations based on
patterns of user preferences.

A Manufacturing Feedback Loop Success Story

In late 2022, one of Europe’s largest automotive parts
manufacturers faced a critical challenge: their quality control
process couldn’t keep pace with production. Traditional
automation was failing to catch subtle defects that human
inspectors could spot instantly. The solution they implemented
would transform not just their quality control but our
understanding of how AI agents learn.
“We had tried everything,” recalls their Head of Operations.
“Standard computer vision, rule-based systems, even basic AI

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models. But defects kept slipping through. What we needed

wasn’t just a smarter system—we needed one that could learn
and adapt like our best human inspectors.”
The breakthrough came with the implementation of an AI
agent equipped with sophisticated feedback loops. Instead of
just flagging defects, the system tracked the outcomes of its
decisions, learning from both successes and failures. When it
correctly identified a subtle defect, that pattern strengthened
in its memory. When it missed one, it adjusted its detection
parameters.
But the real magic happened three months into the deploy-
ment. The system began identifying potential defects that even
experienced inspectors hadn’t noticed—subtle patterns that pre-
ceded more obvious flaws. “It was like having an inspector who
could not only spot problems but predict them,” explains their
Quality Control Director. “The AI wasn’t just learning from feed-
back—it was discovering new insights we hadn’t even consid-
ered.”
The results were transformative: quality control accuracy
improved by 32%, while inspection time decreased by 45%.
More importantly, the system continued to improve month after
month, identifying new patterns and refining its understanding
of the manufacturing process.

The Strategic Advantage

For business leaders, feedback loops offer more than just
incremental improvement—they provide a strategic advantage
in the AI arms race. Companies that embrace feedback loops
can outpace competitors by building systems that adapt faster,
learn better, and stay relevant longer. In a world where the
pace of change is accelerating, the ability to evolve is not just
an asset; it’s a necessity. By integrating feedback loops into
their AI agents, businesses can unlock new levels of efficiency,
personalization, and innovation.

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 Memory: Building AI That Learns

Feedback loops address some of the most pressing challenges

in AI implementation. Consider customer experience. An AI
agent designed to handle customer inquiries might perform
well on launch day, but what happens when users begin asking
new types of questions or expressing frustration in novel ways?
Without feedback loops, the agent stagnates, offering irrelevant
responses and driving customer dissatisfaction. With feedback
loops, the chatbot learns from every interaction, adjusting its
responses to better meet customer needs.
Feedback loops also shine in dynamic industries like
e-commerce, where AI agents must react to changing demand
patterns, inventory levels, and competitor pricing. They enable
systems to stay relevant, ensuring that recommendations and
decisions are always in tune with current realities.
Let’s illustrate the impact of feedback loops with two real-
world examples we worked on. The first one is very interesting
because it shows how AI agents can optimize industrial efficiency
by predicting and preventing costly failures in manufacturing.
This highlights how feedback loops not only improve technical
accuracy but also directly contribute to measurable business
outcomes, such as increased profitability and reduced downtime.

Example 1: AI Agent for Predictive Maintenance in

Manufacturing
1. Initial Task: The AI agent monitors factory machinery
to predict potential breakdowns and minimize downtime.
For example, it analyzes data from sensors tracking
vibration, temperature, and wear.

2. Action Generation: Based on the retrieved insights, the

AI generates actionable recommendations:

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 Agentic Artificial Intelligence

“The vibration pattern suggests bearing wear in Machine

X. Schedule a bearing replacement within the next 72
hours to prevent failure.”

3. Automated Feedback Through Revenue Metrics:

• The system tracks the financial outcomes of its
actions using predefined indicators, such as reduced
downtime, lower repair costs, or increased output.
• If the maintenance intervention prevents a breakdown,
it records this as a positive outcome and links it to the
specific recommendation and retrieved data.

4. Positive Reinforcement Learning:

• The AI reinforces the association between vibration
patterns and bearing wear in its predictive model.
• It flags the retrieved data as highly relevant for
similar issues, improving its retrieval accuracy for
future anomalies.

5. Updating the memory:

• Maintenance logs and outcomes from this event are
added to the database, creating new knowledge the
system can draw from in the future.
• The system also incorporates cost-benefit analysis,
associating specific actions with the revenue saved
or generated.

6. Adaptive Behavior: Over time, the AI becomes better

at identifying subtle signs of failure earlier, optimizing
its recommendations to reduce costly downtime. It may
also learn to prioritize actions based on financial impact,
ensuring the most critical interventions are addressed
first.

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 Memory: Building AI That Learns

As an outcome, the AI agent maximizes efficiency by

minimizing production losses, reducing expensive emergency
repairs, and increasing the overall efficiency of the manufacturing
process. Each successful prediction and intervention refines
its models and retrieval database, enhancing its long-term
profitability.

Example 2: AI Agent for Personalized Product

Recommendations
The second example demonstrates the transformative potential
of feedback loops in e-commerce, where personalization drives
customer satisfaction and revenue growth. It showcases how an
AI agent learns from user behavior to refine its recommendations,
creating a cycle of continuous improvement that aligns perfectly
with dynamic market demands and individual preferences.

1. Initial Interaction: A user visits an e-commerce site

and searches for “comfortable running shoes for trail
running.” The AI agent processes this input and generates
an embedding that captures the user’s preference for
comfort and trail-specific footwear.

2. Response Generation: The AI generates a tailored

recommendation:
“Based on your search, we recommend the TrailMax
Comfort Runner, designed for rugged terrains with extra
cushioning. It’s available in your size for $120. Would
you like to see reviews or add it to your cart?”

3. Automated Feedback Collection:

• The agent tracks whether the user clicks the
recommendation, adds the product to the cart, or
completes the purchase.
• Positive signals (e.g., a purchase or click-through)
reinforce the recommendation’s success.

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 Agentic Artificial Intelligence

• Negative signals (e.g., the user ignores the suggestion

or continues searching) indicate the need for
improvement.

4. Positive Reinforcement Learning:

• If the user buys the TrailMax Comfort Runner, the
system treats this as a positive outcome, reinforcing
similar recommendations for future users with
similar queries.
• The AI updates its embeddings, associating comfort
and trail running more strongly with products that
perform well in sales and customer satisfaction.

5. Updating the memory:

• Products with consistently high conversion rates for
specific queries are flagged as top-performing and
prioritized in future recommendations.
• The AI also integrates user-generated feedback, such
as reviews and ratings, into its database to improve
its understanding of customer preferences.

6. Adaptive Behavior: Over time, the AI learns to prioritize

products that align with user preferences and have a
higher likelihood of generating revenue. For instance,
it may start suggesting slightly higher-priced items
with better reviews if they have a strong track record of
driving purchases.

When another user searches for “trail running shoes,” the

AI immediately suggests top-performing, high-revenue products
tailored to similar preferences, improving the chances of
conversion and increasing revenue. This feedback loop ensures
that the agent’s behavior continuously adapts to customer
preferences and business goals.

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Performance Today: What Feedback Loops

Deliver
Feedback loops are already delivering transformative perfor-
mance improvements in various industries. Today, based on our
experience, AI agents equipped with well-designed feedback
loops can achieve remarkable feats of adaptability and preci-
sion. For example, they can improve their response accuracy by
over 20% within weeks of deployment by analyzing and learn-
ing from user interactions. In e-commerce, recommendation en-
gines powered by feedback loops often see click-through rates
increase by 10-30% as they adapt to evolving customer prefer-
ences in real-time.
However, performance isn’t uniform across all applications.
The effectiveness of feedback loops depends on the complexity
of the environment and the quality of data. In relatively stable
settings, like fraud detection or inventory management, feedback
loops can lead to near-optimal performance within months. In
more dynamic or unpredictable environments, such as financial
markets or human behavior modeling, the improvement curve
may be slower, but the potential for breakthroughs is immense.

Limitations and Ethical Considerations

To effectively leverage feedback loops, business leaders must
address several critical factors to ensure their AI systems
function optimally and ethically.
A key consideration is timeliness. Feedback that is delayed
or outdated significantly hinders an AI system’s ability to adapt
to dynamic environments. Systems must process and act on data
as close to real-time as possible to remain relevant, especially
in fast-paced industries like finance or logistics. Without timely
feedback, the entire loop can falter, leading to suboptimal
outcomes.

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Robust infrastructure is another key consideration. Feedback

loops require systems capable of handling large volumes of data
efficiently. Cloud platforms such as AWS or Azure provide
scalable solutions for collecting, processing, and analyzing data
at the scale necessary for complex AI systems. Without this
infrastructure, organizations face bottlenecks that can limit their
AI’s adaptability and performance.
Human oversight is critical to avoid unintended consequenc-
es. While feedback loops enable AI systems to operate with sig-
nificant autonomy, their decisions must be monitored to ensure
alignment with organizational goals and ethical standards. Reg-
ular audits of system outputs are essential, both to ensure accura-
cy and to address potential deviations or unintended behaviors.
An ethical concern is user manipulation. In their drive to
optimize outcomes, AI systems—particularly recommendation
engines—can exploit psychological triggers or push users
toward addictive content. While such practices may yield short-
term gains, they risk damaging trust and long-term relationships
with users. Organizations must strike a balance, designing
feedback loops that prioritize user well-being alongside business
objectives.
Overfitting is a common limitation where AI systems become
too focused on optimizing a specific metric, losing sight of the
bigger picture. For instance, a customer service AI might prioritize
reducing response times so much that it sacrifices the quality of
its answers. This happens because the AI learns to “memorize”
patterns that work well for one goal rather than adapting to a variety
of scenarios. To prevent this, multi-objective optimization is used
to balance priorities like speed, accuracy, and user satisfaction,
ensuring the AI performs well across multiple areas without
becoming too narrowly focused.
In practice, successful feedback loop implementation requires
careful design, vigilant monitoring, and ethical foresight. When
done right, feedback loops can drive AI systems to continuously
improve and adapt, providing immense value to businesses.

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 Memory: Building AI That Learns

The Evolution Ahead: Near-Term and Long-

Term Outlook
In the near term, we can expect feedback loops to become more
seamless and automated. Advances in real-time data processing
and edge computing are already enabling faster and more efficient
feedback cycles. For instance, real-time personalization in marketing
will become more refined as AI systems integrate instantaneous
user responses to adjust their strategies dynamically. In logistics,
AI-powered feedback loops will revolutionize operations by
responding to traffic conditions, weather changes, and demand
fluctuations on the fly, reducing delivery times and costs.
Over the next few years, the integration of reinforcement
learning with advanced feedback mechanisms will push
boundaries further. Agents will not only learn from individual
actions but also develop the ability to generalize learning
across tasks. For example, a warehouse robot trained to stack
boxes could transfer its learning to other tasks, like assembling
products, with minimal retraining. This cross-task adaptability
will significantly expand the applicability of feedback loops,
making AI systems more versatile and resilient.
In the longer term, AI agents will likely integrate multiple
feedback loops simultaneously, each addressing a different aspect
of performance, such as speed, quality, and user satisfaction.
Imagine an AI agent that not only learns your schedule but also
adapts its tone, style, and even its level of proactivity based on
nuanced feedback from your interactions.
Recent work by Hospedales et al. suggests we’re approaching
what they call “meta-learning systems”—AI agents that
don’t just learn from experience but learn how to learn more
effectively.133 Their early experiments show these systems

133
Timothy Hospedales, et al., 2020. “Meta-Learning in Neural Networks: A
Survey.” https://arxiv.org/abs/2004.05439 .

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adapting to new situations up to 3 times faster than traditional

learning approaches.
Self-optimizing structures and autonomous evolution
represent two complementary aspects of structural adaptability
in AI. Self-optimizing structures focus on task-specific
adjustments, where feedback loops enable AI systems to
dynamically reconfigure their architecture in real-time for
efficiency and performance. For instance, recent research
showcases how AI can optimize itself layer by layer during
training, ensuring it remains relevant and resource-efficient in
the face of immediate demands.134
Autonomous evolution, however, takes this adaptability
further. As demonstrated by recent studies,135 AI agents can
use feedback not only to optimize for the present but to evolve
entirely new frameworks that prepare them for future tasks or
environments. This process mirrors biological evolution, where
survival depends on iterative growth and adaptation, making
AI systems capable of tackling challenges beyond their initial
design.

***

Together, these advancements show the transformative potential

of feedback loops in shaping AI systems that are not only more
knowledgeable but also structurally adaptable. This evolution
positions feedback as the driving force behind AI that learns,
evolves, and aligns with both practical and human-centered

134
Álvaro G. Díaz and Hugues Bersini, 2020. «Self-Optimisation of
Dense Neural Network Architectures: An Incremental Approach.» 2020
International Joint Conference on Neural Networks (IJCNN). https://doi.
org/10.1109/IJCNN48605.2020.9207416.
135
Zhenhao Shuai, et al., 2023. “A Self-adaptive Neuroevolution Approach
to Constructing Deep Neural Network Architectures Across Different Types.”
https://arxiv.org/abs/2211.14753

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 Memory: Building AI That Learns

goals, offering a glimpse of a future where machines grow

alongside humanity.
Understanding how memory works in AI is one thing—
implementing it successfully is another challenge entirely. As
one technology leader at a Fortune 100 company told us, “We
thought adding memory to our AI would be like upgrading
computer RAM. Instead, it was like teaching a child how to learn
from experience—complex, nuanced, but incredibly powerful
when done right.”
The implementation stories ahead reveal both the pitfalls
and the proven paths to success. You’ll discover why some
organizations achieve transformative results with memory-
enabled AI while others struggle to see any benefit. More
importantly, you’ll learn the practical steps to ensure your
implementation lands on the right side of this divide.
Through our work with hundreds of organizations, we’ve
distilled these lessons into a clear framework for success.
Whether you’re just starting your journey with AI memory or
looking to enhance existing

Best Practices in Managing Memory for

Agents
Leading Teams to Use Agent’s Memory the
Right Way
Our experience has shown that implementing memory in AI
agents is only the starting point. To unlock its full potential, it’s
critical to adopt practices that ensure this capability delivers
measurable benefits while avoiding inefficiencies or risks. Like
any powerful tool, the true value of memory depends on how
it’s used. Over the years, we’ve developed strategies that allow
businesses to leverage AI memory effectively, minimize pitfalls,
and maintain trust.

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 Agentic Artificial Intelligence

First, relevance is key. AI memory systems are only as

effective as the guidance they receive. Users play an active
role in shaping what the AI retains by emphasizing critical
information during interactions. This might involve repeating
key points, explicitly stating their importance, or using
structured protocols to tag certain information as “important”
or “archivable.” For instance, in project management, users
might highlight milestones, decisions, and obstacles that are
vital for long-term tracking. However, the goal isn’t for the AI to
remember everything—it’s to retain the information that drives
better outcomes. Avoiding memory overload by filtering out
inconsequential details ensures that the system remains focused
and efficient.
Another best practice is leveraging summarization to refresh
context. AI agents equipped with memory excel when they can
seamlessly recall past interactions, but users may not always
remember what the AI knows. Summarization bridges this gap,
allowing users to align with the AI’s memory. Asking for a recap—
such as “What are the key takeaways from our last session?” or
“Can you summarize my priorities for this project?”—ensures
continuity and alignment. Summaries act as checkpoints, helping
users validate and correct the AI’s understanding as needed.
In team settings, this becomes even more powerful. Imagine
a marketing team using an AI assistant to manage campaigns.
Before every weekly meeting, the AI could provide a summary
of ongoing efforts, performance metrics, and lessons from past
campaigns, saving time and keeping everyone on the same page.
Memory audits are another critical aspect of effective
memory management. Just as humans periodically reflect to
clarify thoughts, AI systems benefit from regular reviews of
their memory. These audits help identify irrelevant, outdated,
or incorrect information and allow users to refine what the AI
retains. For example, a customer service AI might still hold on
to a customer’s old address or obsolete purchasing habits. By

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 Memory: Building AI That Learns

reviewing and cleaning the AI’s memory, businesses can ensure

accuracy and trustworthiness. Structured audit protocols, such
as scheduled memory reviews with teams, can refine the AI’s
priorities and align its memory with business goals.
Balancing memory depth and privacy is perhaps the most
sensitive aspect of managing AI memory. While memory enables
deep personalization and contextual understanding, it also
requires users to trust the AI with sensitive data. Transparency
is non-negotiable in fostering this trust. Businesses must clearly
communicate how memory is managed and offer users control
over their data. This includes features that allow users to view
what the AI remembers, edit or delete specific memories, and set
boundaries for what the AI can retain. Prioritizing secure storage
practices, such as compartmentalizing sensitive information and
applying strict access controls, is essential.
From our experience, the successful implementation of
memory in AI systems requires a proactive approach that combines
relevance, summarization, auditing, feedback, and privacy.
By adopting these best practices, businesses can transform AI
memory into a powerful asset that enhances efficiency, builds
trust, and drives meaningful outcomes. The journey doesn’t end
with implementation—it’s an ongoing process of refinement,
alignment, and ethical management to ensure that the AI system
evolves alongside the needs of the organization.

Addressing Privacy Concerns and Ensuring

Transparency
Managing privacy and ethical issues in AI memory systems
is critical, as long-term memory retention involves handling
sensitive user data. AI agents, especially those with memory
capabilities, can collect and store information such as personal
preferences, interaction histories, or even behavioral patterns.
While these features enable personalization and improve user

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experiences, they also introduce significant risks to data privacy

and compliance with regulations like the General Data Protection
Regulation (GDPR) or the California Consumer Privacy Act
(CCPA).
The first step in managing privacy is minimizing data
collection to only what is essential. Leaders should adopt a
privacy-by-design approach, ensuring data privacy is a core
consideration from the start. For example, implement mechanisms
that anonymize or pseudonymize data before it’s stored in long-
term memory. By removing direct identifiers, even in the event
of a breach, sensitive information is protected. Additionally, use
techniques like data minimization, which restricts the storage
of unnecessary or overly detailed data, and regular audits to
identify and remove outdated or irrelevant information.
Compliance with regulations such as GDPR and CCPA is
another essential aspect. These laws give users rights over their
data, such as the ability to access, delete, or restrict how their
information is used. AI memory systems must be designed to
respect these rights. For instance, if a user asks an AI system
to “forget” specific information, the system should have
mechanisms to erase the corresponding data from its databases,
including backups and long-term storage. Moreover, companies
must ensure transparency, clearly communicating to users what
data is being collected, how it will be used, and how long it will
be retained.
From a practical standpoint, robust encryption for both data
at rest and in transit is non-negotiable. Leaders should invest in
secure database technologies and follow industry best practices
for data storage. Access to memory systems should be restricted to
authorized personnel or processes, with activity logs maintained
to track who accessed what data and when. Implementing
these controls reduces the likelihood of unauthorized access
or breaches, which can severely damage user trust and lead to
regulatory penalties.

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 Memory: Building AI That Learns

Ethical issues go beyond privacy to include concerns like

bias and misuse of data. AI agents with memory capabilities
might reinforce biases if they overly rely on historical data
that reflects outdated or skewed perspectives. Leaders must
ensure that their AI systems undergo continuous bias testing
and validation. Additionally, organizations should establish
clear policies around the ethical use of AI memory systems,
including defining acceptable boundaries for data retention and
personalization to prevent intrusive or manipulative practices.
In summary, managing privacy and ethical issues in AI
memory is not just about compliance; it’s about building trust
and fostering responsible AI use. By prioritizing secure data
handling, aligning with regulations, and addressing ethical
concerns proactively, leaders can ensure their AI systems are
both effective and respectful of user rights and expectations.

Balancing Innovation and Privacy: A Healthcare

Provider’s Journey
When a leading U.S. healthcare provider decided to implement
memory-enabled AI for patient care coordination in 2024,
they faced a seemingly impossible challenge: how to create
an AI system that could learn from patient interactions while
maintaining strict HIPAA compliance and patient privacy.
“The stakes couldn’t have been higher,” their Chief Privacy
Officer tells us. “We needed our AI to remember and learn from
patient interactions to provide better care, but one privacy breach
could have devastating consequences.”
Their solution combined innovative technology with careful
governance. They implemented a multi-layered memory
architecture where patient-identifying information was strictly
separated from behavioral patterns and clinical insights. The
AI could learn from aggregated patterns without accessing
individual patient details, using what they call “privacy-
preserving learning patterns.”

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 Agentic Artificial Intelligence

The technical implementation was just the beginning. They

established a clear governance framework that included:

• Regular privacy audits of the AI’s memory systems

• Automated detection of potential privacy risks
• Clear protocols for data retention and deletion
• Patient control over their data through a transparent
portal

“What surprised us most,” their CTO reflects, “was how this

privacy-first approach actually improved the AI’s effectiveness.
By focusing on patterns rather than individual details, the system
developed more robust and generalizable insights.”
One year after implementation, the results spoke for
themselves: a 40% improvement in care coordination efficiency,
with zero privacy breaches. More importantly, patient trust in the
AI system, measured through regular surveys, reached 87%—
higher than their trust in some traditional hospital systems.

Recommendations for Technical

Implementation
To succeed in implementing these layers, it’s essential to
combine the right tools, strategies, and technologies—each
with its unique technical requirements and challenges. Based on
our experience, here’s a practical guide to help you and your
technical teams navigate these complexities, complete with the
enablers needed, common pitfalls we’ve observed, and solutions
to overcome them.

248
 Layer Implementation Databases/Enablers Common Pitfalls How to Mitigate
Layer 1: Use context windows AI frameworks (e.g., Context overflow—losing Implement
Short-Term to handle immediate OpenAI GPT, Langchain), critical details when the summarization
Memory tasks, with attention optimized for token limits. input exceeds the token techniques
mechanisms to limit. to condense
prioritize key information without

Memory: Building AI That Learns

information. losing meaning.
Layer 2: Categorize retained Relational databases Difficulty in categorizing Use tagging and
Long-Term information into (PostgreSQL) for structured information correctly, metadata to index
Memory episodic (events), data; NoSQL databases leading to retrieval information properly
semantic (facts), and (MongoDB) for flexible inefficiencies. and implement
procedural (how-to) storage. Pinecone for vector automated
249

memories. store and Neo4j for graphs. categorization

workflows.
Layer 3: Continuously Feedback logs stored in Ignoring or misinterpreting Develop clear
Feedback improve through user databases like BigQuery feedback, leading to poor metrics for success,
Loop feedback, adjusting or Amazon DynamoDB; adaptations. and use tools like
memory prioritization analytics tools like website reinforcement
and retrieval analytics. learning to align the
strategies. AI’s updates with
these metrics.

Table 7.1: Recommendations for implementing the three layers of memory (Source: © Bornet et al.)
 Agentic Artificial Intelligence

Each layer in the AI memory framework builds upon the

previous one, relying on seamless integration between databases
and memory functions to operate effectively. Common pitfalls,
such as poorly tuned systems or unstructured data, can lead to
inefficiencies in memory retrieval or unreliable performance.
Mitigation requires maintaining clean, well-organized data,
setting clear priorities for memory use, and continuously
monitoring and refining the system. Addressing these challenges
ensures that AI remains efficient, scalable, and adaptable to real-
world applications.

Data Management as the Foundation of

Success
Data management is the backbone of effective AI systems,
especially those equipped with memory capabilities. Just like a
business relies on well-organized records to operate efficiently, an
AI agent depends on seamless data storage, retrieval, and updating
to perform at its best. If the underlying data infrastructure is poorly
managed, even the smartest AI can stumble—retrieving outdated
information, missing critical details, or delivering inconsistent
outputs. For businesses, this can mean missed opportunities,
frustrated customers, and diminished trust.
To avoid these pitfalls, leaders must view data management
as a strategic priority. This starts with investing in scalable and
efficient database technologies that can handle the growing
demands of modern AI systems. Imagine an AI-powered
customer service agent that needs to access thousands of
customer profiles, purchase histories, and past interactions in
seconds. Without a robust infrastructure, the agent could lag or
deliver incomplete responses, eroding the seamless experience
users expect. Technologies like cloud-based databases, real-time
analytics tools, and vector storage for semantic searches are key
enablers for making AI memory systems agile and effective.

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 Memory: Building AI That Learns

Seamless integration between databases and AI systems is

another critical piece of the puzzle. AI memory systems rely
on smooth, real-time interactions with the data layer to retrieve
the most relevant and up-to-date information. This isn’t just
about technical compatibility—it’s about designing workflows
where data flows effortlessly between storage, processing, and
the AI agent. For example, in a retail setting, an AI system
recommending products needs to instantly incorporate the latest
inventory updates or customer browsing behavior to make its
suggestions relevant. Without this dynamic connectivity, even
the best AI models can appear out of touch.
Data management also requires vigilance to ensure quality
and accuracy. Poor data leads to poor decisions, both for
humans and AI. Leaders must establish processes to clean,
update, and validate data regularly, ensuring that what the AI
learns and retrieves is both reliable and current. Additionally, as
systems scale, leaders should consider tools that automate these
processes, reducing manual errors and streamlining operations.
In essence, data management is the hidden force that powers
AI’s ability to deliver value. It’s not just about having data; it’s
about organizing, accessing, and updating it in ways that unlock
the full potential of AI. Business leaders who invest wisely in
their data infrastructure not only future-proof their systems but
also position their companies to thrive in an increasingly AI-
driven world.

The Memory Revolution Has Just Started

Throughout this chapter, we’ve explored how memory transforms
AI from a sophisticated calculator into a genuine thinking
partner. We’ve seen how short-term memory enables coherent
conversations, while structured retention organizes experiences
for long-term learning. Perhaps most crucially, we’ve understood
how feedback loops drive continuous improvement, allowing AI
systems to learn and adapt from every interaction.

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The implications for business leaders are profound and

immediate. First, memory-enabled AI agents fundamentally
change the economics of customer interaction. When AI agents
can maintain context across conversations, remember customer
preferences, and learn from past interactions, they deliver
exponentially better experiences at a fraction of the cost. We’ve
seen this in practice with companies achieving 40-50% reductions
in customer service costs while simultaneously improving
satisfaction scores.
Second, memory transforms decision-making processes.
Rather than relying solely on current data, leaders can now tap
into AI agents that remember and learn from every past decision,
success, and failure across the organization. This institutional
memory, previously scattered across emails, documents, and
employees’ minds, becomes a structured, accessible resource for
informing strategy and operations.
Third, memory-enabled agents reshape how organizations
learn and adapt. When AI agents can remember and analyze
patterns across thousands of projects or millions of customer
interactions, they identify opportunities and risks that would be
impossible for humans to spot alone. This isn’t replacing human
judgment—it’s augmenting it with a level of pattern recognition
and historical awareness previously unimaginable.
As we close this chapter, remember that the development of
memory in AI represents more than just technological progress—
it marks a fundamental shift in how we interact with machines
and how they help us think about the world. The question facing
business leaders isn’t whether to embrace this transformation
but how to shape it in ways that create value while respecting
human agency and creativity.

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 PART 3
ENTREPRENEURSHIP
AND PROFESSIONAL
GROWTH WITH AI
AGENTS
 Agentic Artificial Intelligence

N
ow that we’ve explored the foundational keystones
of Action, Reasoning, and Memory, it’s time to turn
to hands-on practice. How do we actually build these
systems? And, more importantly, how can you leverage them to
create real value?
In the previous Parts of the book, we’ve taken you on a
journey from understanding what AI agents are to discovering
how they think, act, and learn. We’ve seen how they represent
a fundamental shift from traditional AI systems—not just
processing information but autonomously pursuing goals on our
behalf. But knowing about this technology isn’t enough. The
real question is: how can you harness it?
The coming chapters will equip you with the tools to turn
the transformative potential of AI agents into tangible reality.
The future belongs to those who can not only understand this
technology but effectively implement it—and that’s exactly
what we’re about to show you how to do.
Here, we roll up our sleeves and get practical. Whether
you’re looking to transform your organization or launch the next
million-dollar business, these chapters provide your roadmap
from idea to implementation.
Let’s begin our journey from ideas to implementation.

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CHAPTER 8
A PRACTICAL GUIDE FOR
BUILDING SUCCESSFUL
AI AGENTS

“O
ur newsletters are killing us,” we remember
thinking one late Friday night, poring over dozens
of articles, trying to craft summaries that would
engage our readers. “There has to be a better way.” Fast forward
one month: our agentic system was handling the entire process,
our subscriber base had exploded to 300,000 in just one month,
and our team had reclaimed 40 hours a week for creative work.
The best part? We were producing better content than ever
before.
But here’s what makes this story relevant to you: building
effective AI agents isn’t about having the biggest budget or the
most advanced technology. It’s about understanding a few key
principles that separate success from failure. In this chapter,
we’ll share these principles through our own trials, errors, and
breakthroughs in implementing AI agents across industries.
We won’t just tell you what to do—we’ll show you. Through
real examples, practical tools, and honest accounts of what went

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wrong (and how we fixed it), you’ll gain a blueprint for building
AI agents that transform how you and your organization work.

Step 1: Finding the Right Agentic

Opportunities
Picture yourself in the bustling office of a fast-growing digital
marketing agency. Jenny, the founder and creative director, sits
at her desk surrounded by multiple screens. She’s frantically
switching between applications—pulling social media analytics,
checking campaign performances, organizing content calendars,
and trying to compile everything into client reports.
Jenny approached us with an intriguing challenge: “My team
is drowning in routine tasks,” she explained. “We have brilliant
creatives spending hours on data entry and report generation
instead of strategy and innovation. But how do I know which
tasks are really right for AI agents?”
This question—knowing where to start with AI agents—
is crucial. We’ve learned that success often depends more on
choosing the right opportunities than on technical sophistication.
Let’s start with a fundamental truth: AI agents aren’t magical
solutions that can handle any task. Just as you wouldn’t use a
hammer to fix every home repair problem, not every business
challenge calls for an AI agent. In fact, one of the most common
pitfalls we see is entrepreneurs and business executives rushing
to implement agents without first determining if they’re the right
tool for the job.

When Not to Use AI Agents

Let us start by recognizing where not to deploy AI agents.
Through our experience, we have identified several red flags.
First, tasks that require genuine human creativity or
emotional intelligence should generally remain human-driven.
At the marketing agency, AI agents could handle data gathering

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and basic reporting, but creative campaign ideation and client

relationship management remained firmly in human hands.
While Level 3 agents can engage in natural language interactions,
they cannot truly capture the emotional resonance needed for
compelling marketing campaigns.
Similarly, strategic decision-making that requires under-
standing the broader market context or making judgment calls
based on incomplete information should stay with humans.
Even at Level 3, AI agents lack the sophisticated reasoning and
market intuition necessary for these scenarios.
Some tasks are simply too complex for AI agents to handle
effectively. A technology company once asked us to build an
agent to manage their entire customer support operation. While
the potential impact was significant, the process involved too
many unique scenarios and emotional interactions. AI agents
perform best when their capabilities align with the complexity
of the task they are assigned.
In other cases, AI agents may lack the authority to make
critical decisions. A financial services firm wanted an AI agent
to make investment decisions autonomously. This was not only
risky but also a clear violation of regulatory requirements. It is
essential to consider whether an AI agent has the appropriate
level of authority for the task it is assigned.
By understanding these limitations, organizations can ensure
they deploy AI agents where they add value while keeping
human oversight where it matters most.

The Three Circles of Agentic Opportunity

We’ve developed a straightforward but powerful approach we
call “The Three Circles of Agentic Opportunity” to help identify
the perfect sweet spots to implement with agentic AI. Picture
three overlapping circles. The sweet spot for your AI agents lies
where these circles intersect. Let’s break this down through our
marketing agency’s experience.

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 Agentic Artificial Intelligence

Figure 8.1: The Sweet Spot for Agentic Opportunities (Source: © Bornet et al.)

Circle 1: High Impact - Will It Matter?

The first circle represents tasks that, if automated, would
significantly impact your organization. The consideration is
simple: if you automate this process, will it make a meaningful
difference? Impact isn’t just about saving time—it’s about what
that saved time enables your organization to do.
Consider the routine tasks that consume your skilled
professionals’ time. Perhaps your sales team spends hours
updating CRM records instead of building relationships with
clients. Maybe your researchers spend more time formatting data
than analyzing it. Or your HR team might be buried in processing
routine requests rather than focusing on employee development.
The highest-impact opportunities often aren’t your most
complex processes. Instead, look for the routine tasks that are
preventing your best people from doing their best work. When

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evaluating impact, ask: If this task were automated tomorrow,

what would your team be able to accomplish instead?
For the marketing agency, their monthly client reporting
process was consuming over 200 hours of team time per month.
More importantly, this routine work was preventing their analysts
from doing the strategic thinking their clients really needed.

Circle 2: Feasibility - Can It Be Done?

The second circle is about whether current AI agent technology
can actually handle the task effectively and safely. Think of
feasibility like checking if you have the right ingredients before
starting to cook—you need to ensure you have all the essential
elements for success.
The most feasible processes for automation typically have
these characteristics:

• Clear, consistent rules for making decisions

• Accessible data and systems
• Definable success criteria
• Manageable consequences if something goes wrong
• Ability to verify results before they impact operations

The key is to look for processes where you can explain

the rules without using phrases like “it depends” or “but
sometimes...” too often. The more exceptions and judgment
calls a process requires, the less suitable it is for your first AI
agent project.
For the marketing agency, the reporting process was feasible
for automation because it had all the key ingredients. First, the
data they needed was accessible through APIs. In addition, the
process followed consistent rules, and the outcome could be
easily checked, while errors could be managed by people.

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Circle 3: Effort - Is It Worth It?

The final circle considers the practical aspects of implementa-
tion—the resources, time, and organizational change required.
This isn’t just about technical complexity; it’s about your orga-
nization’s readiness for change.
Consider whether:

• The process is well-documented

• Your team is ready and willing to adapt
• You can start small and scale up
• The potential benefits clearly justify the investment
• You can implement without disrupting core operations

The best first projects can often be implemented in phases,

allowing you to build confidence and capabilities gradually.
In Jenny’s case, automating reports was attractive because
they already had documented procedures, they could start small
and scale up, and the team was eager for change.

Finding Your Sweet Spot

The ideal AI agent projects live where these three circles
overlap. For Jenny’s agency, report automation was perfect
because it would free up hundreds of hours of valuable analyst
time (Impact), the process was well-defined and safe to automate
(Feasibility), and they could implement it without disrupting
their core business (Effort).
Start by mapping your own processes against these three
circles. Look for tasks that your team repeatedly complains
about—these often signal high-impact opportunities. Then,
evaluate whether these tasks have clear rules and accessible data.
Finally, consider whether you have the resources and readiness
to tackle the change.
Remember, your first AI agent project should be like a good
first date—ambitious enough to be exciting but not so complicated

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that it’s likely to end in disaster. Start with something that scores
well across all three circles, and you’ll build both success and
momentum for more ambitious projects to come.

Implementation Reality Check: Lessons from the

Field
Our experience has revealed some crucial realities about finding
the right opportunities:
First, companies rarely know which agents they actually
need. While they often approach us with specific agent ideas, in
nearly half the cases, these aren’t the most valuable automation
opportunities. This is why our Three Circles framework is so
critical—it helps cut through assumptions to identify what truly
matters. When a client insisted on automating their social media
posting, our analysis revealed that automating lead qualification
would deliver five times the ROI.
Second, many companies believe they can fully automate
entire job roles with AI agents, but this is a fundamental
misunderstanding of how AI works today. AI agents are not
employees—they lack the flexibility, adaptability, or judgment
that human workers bring to a role.
A single employee often manages multiple interconnected
tasks that require context-switching, decision-making, and
collaboration—things AI agents still struggle with. Instead of
thinking in terms of “automating roles,” the right approach is to
think in terms of “automating tasks.”
Finally, well-documented processes are gold mines for
agent implementation. When evaluating opportunities, we’ve
found that leveraging existing documented standard operating
procedures (SOPs) dramatically reduces implementation time
and increases success rates. For one financial services client,
their meticulously documented customer onboarding process
allowed us to deploy an agent in half the time compared to a
similar project where we had to map the process from scratch.

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These field lessons have shaped how we approach the op-

portunity identification phase, reinforcing the importance of the
Three Circles framework while adding practical considerations
that go beyond theory.

A Practical Exercise: Finding Your Agentic

Opportunities
Now that you understand the theory, let’s walk through a simple
exercise we use with organizations to identify their best agentic
opportunities. We developed this approach after seeing too many
companies rush into automation without proper analysis, often
leading to wasted resources and frustrated teams.

Step 1: Task Inventory

Begin by gathering your team for a focused two-hour session.
The goal is to create a comprehensive list of recurring tasks
that consume significant time or create bottlenecks in your
operations. We’ve found that asking specific questions yields
better results than general brainstorming.
Ask your team:

• “What tasks do you find yourself doing repeatedly

throughout the week?”
• “Which activities prevent you from focusing on more
strategic work?”
• “What processes consistently create bottlenecks in our
operations?”
• “Which routine tasks require the most oversight to
prevent errors?”

When we ran this exercise with the marketing agency,

their list included tasks like monthly client reporting, social
media analytics, campaign performance tracking, competitor
monitoring, and content calendar management. The key was

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capturing not just the tasks, but also their frequency and the
approximate time invested in each.

Step 2: Impact Assessment

Next, evaluate each task’s potential impact if automated. We
use a simple but effective scoring system that considers multiple
factors. For each task, score the below criteria on a scale of 1-5:

1. Time Saved (How much time does it take today that can
be saved?)
• In most cases, just estimating the average time saved
is enough. A higher score (5) means the time saved
would be very high.
• In some cases, going into more detailed calculations
of the current costs in dollars can be useful. Multiply
hours per instance by frequency and cost per hour
(e.g., 2 hours per task × 20 tasks/month × $50/hour =
$2,000 savings)

2. Strategic Value of Freed Time (What’s the opportunity

cost?)
• The strategic value measures the broader benefits
of automating a task, including its impact on
employee satisfaction, client experience, competitive
positioning, and revenue potential.
• It is rated on a scale of 1-5, with higher scores
indicating greater overall business impact.

3. Error Reduction Potential (How often do mistakes

happen?)
• In most cases, an estimation is sufficient. A higher
score (5) means the errors are highly frequent or
costly.
• In some cases, going into more detailed calculations
of the current costs in dollars can be useful. Calculate

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it using the formula: current error rate × cost per

error × volume (e.g., a 5% error rate × $100 per error
× 1,000 instances/month = $5,000 savings).

4. Scalability Impact (Can this be applied across the

organization?)
• Scalability impact measures how well a solution can
expand without increasing costs at the same rate. A
higher score (5) means the task can be automated and
scaled across the organization with minimal extra
cost.
• For example, automating a client onboarding process
that works for 10 clients can easily scale to 1,000
without needing additional staff, making it highly
scalable (rated 5).

Here is a summary of the four criteria and the meaning of

the scores:

Criteria Score on a scale from 1 to 5

Time 1: Less than an 3: Several 5: Multiple hours
Investment hour per week hours per week daily
Strategic Value 1: Limited 3: Moderate 5: High-value
of Freed Time alternative use strategic value strategic activities
of time blocked
Error Reduction 1: Few errors 3: Occasional 5: Frequent or
Potential occur significant costly errors
errors
Scalability 1: One-off task 3: Moderately 5: Highly scalable
Impact repeatable across the
organization

Table 8.1: Scoring the four criteria of impact assessment (Source: © Bornet
et al.)

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Then, calculate the total score for each task. Add up the
scores across the four criteria. The minimum possible score is 4,
and the maximum is 20.
Finally, compare scores across tasks—The higher the total
score, the stronger the case for automation.
At the marketing agency, their client reporting process
scored high across all categories: it consumed massive time (5),
prevented strategic analysis (5), frequently contained errors due
to manual data entry (4), and could scale across all clients (5).
This gave it a total score of 19 out of 20, making it a prime
candidate for automation.

Step 3: Feasibility Assessment

For each task that scored highly on impact, evaluate its feasibility
for automation. This helps determine whether current AI agent
technology (Levels 1-3) can reliably handle the task. We break
feasibility into two key components, each scored on a scale of
1-5.

1. Process Standardization
Automation works best when tasks have clear and
structured steps. Evaluate if the process has clear steps,
decision rules, and exceptions. Ask: ‘Can someone new
follow this process exactly using only our documentation?’

2. Data and System Access

AI needs structured and accessible data to function.
If data is scattered, locked in old systems, or requires
human intervention, automation will be difficult. Test
whether data is structured, easily retrievable, and
available through modern systems or requires manual
preparation.

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Use the following scale to rate the process standardization

and data readiness:

Criteria Score 1 Score 3 Score 5

Process 1: Process 3: Basic 5: The process is fully
Standardization is largely documentation documented with
ad-hoc with exists but clear steps, decision
no standard relies heavily rules, and exceptions.
approach. on employee
experience.
Data and 1: Essential 3: Data is 5: All data is
System Access data is available structured, and
locked but requires systems have modern
in legacy significant APIs.
systems or preparation.
paper-based.

Table 8.2: Scoring the two criteria of feasibility assessment (Source: ©

Bornet et al.)

By scoring each task on Process Standardization and Data

and System Access, you will quickly identify which high-impact
tasks are ready for automation and which need improvements
first.

Step 4: Implementation Effort

Once high-impact tasks are identified and their feasibility
is confirmed, the next step is evaluating how difficult it is to
build an AI agent for them. Implementation effort measures the
technical complexity involved, helping you prioritize AI agents
that can be deployed efficiently.
How to Score Technical Complexity (1-5, Reverse Scored)
This is a reverse-scored metric, meaning lower scores indicate
higher complexity and greater difficulty in implementation.
Here’s how to assess it:

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• Score 5 (Easiest to Implement)—The task can be

automated with standard tools that require little or no
customization.
• Score 4—Some customization is needed, but it relies on
widely used technologies.
• Score 3—The solution requires significant custom
development, such as scripting.
• Score 2—Complex integrations or new technology
adoption is required.
• Score 1 (Most Difficult)—The task demands cutting-
edge solutions or extensive research and development
(R&D).

Our recommendation is to always start with proven,

mainstream tools and platforms before pushing technological
boundaries. Automating simple, high-impact tasks first builds
momentum, reduces risk, and ensures early wins before tackling
more complex solutions.

Putting It All Together: The Final Analysis

After spending months helping organizations evaluate AI
agent opportunities, we’ve developed a systematic approach to
making the final implementation decision. Let’s return to Jenny’s
marketing agency to see how this works in practice.
First, we combine the scores from our previous analyses:

• Impact Score (maximum 20 points from our four criteria)

• Feasibility Score (maximum 10 points from process and
data evaluation)
• Implementation Effort Score (maximum 5 points, reverse
scored)

This gives us a total possible score of 35 points. We find

this detailed scoring particularly valuable because it forces

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organizations to think through all aspects of the opportunity

systematically.
For Jenny’s client reporting process, the scores broke down
like this:

• Impact: 19/20 (high time investment, strategic value,

error reduction, and scalability)
• Feasibility: 8/10 (well-documented process, accessible
data through APIs)
• Implementation Effort: 4/5 (standard tools available,
minimal customization needed)

Total Score: 31/35, indicating an excellent candidate for AI

agent implementation.

The Final Touch: The Agentic AI Prioritization

Matrix
While the comprehensive score is helpful, we’ve found that
visualizing opportunities helps teams and management make
better decisions. We plot potential projects on a matrix with two
axes:

• Vertical Axis: Complexity of the Transformation (com-

bining Feasibility and Effort scores)
• Horizontal Axis: Business Impact (using our Impact
score)

This creates four quadrants:

1. Quick Wins (High Impact, Low Complexity): Your ideal

agentic opportunities
2. Strategic Projects (High Impact, High Complexity):
Future opportunities requiring careful planning
3. Low Priority (Low Impact, Low Complexity): Nice-to-
have automations

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4. Avoid (Low Impact, High Complexity): Not worth the

effort

For the marketing agency, client reporting landed squarely

in the Quick Wins quadrant: high impact with relatively low
complexity. This became their first AI agent project, leading to
the successful transformation we described earlier.

Figure 8.2: The agentic AI prioritization matrix (Source: © Bornet et al.)

A Final Note on Selection

If this is your first agentic implementation, note that it will set
the tone for future automation initiatives. So, in addition to the
above criteria, it is important to choose an opportunity that:

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• Will show clear, measurable results within 3-6 months

• Affects enough people to build broad support
• Has a champion willing to oversee the implementation
• Can serve as a learning experience for your organization

Success with your first agent builds confidence and creates

momentum for more ambitious projects. The marketing agen-
cy’s successful reporting automation led to five more agent
implementations over the next year, each building on lessons
learned from the first.

Step 2: Defining AI Agents’ Role and

Capabilities
After identifying the right opportunities for AI agents, the next
crucial step is defining exactly what kind of agent you need.
Think of this like writing a job description—you need to be
crystal clear about the role, responsibilities, and required
capabilities of your digital worker.
Let’s return to the digital marketing agency, where Jenny
and her team have decided to start with automating their
monthly client reporting process. “I know I want to automate
this process,” Jenny told us, “but I’m not sure what kind of
agent I need. Should it be something simple that just pulls data,
or something more sophisticated that can actually write report
analyses?”
This is a common question we hear from business leaders,
and the answer lies in understanding the different levels of AI
agent capabilities and matching them to your specific needs.

Understanding Agent Levels: From Simple to

Sophisticated
Think of AI agents as employees with different skill levels.
Using our Agentic AI Progression Framework, we can broadly

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categorize them into five levels, though currently, only levels

1-3 are commonly deployed in production environments. To be
precise, Level 2 (Intelligent Automation) and Level 3 (Agentic
Workflow) are the most common, so we will focus on these two
levels.
Level 2 agents are more like experienced professionals who
can follow simple, unchanging instructions. Level 3 agents are
like senior professionals who can understand the context and
manage more sophisticated tasks.
For the marketing agency’s reporting process, Jenny needed
to determine which level would be most appropriate. Let’s walk
through the decision-making process they used.

The Decision Framework

To choose the right agent level, we evaluate three key criteria
that help determine whether a Level 2 or Level 3 agent is more
appropriate:

1. Task Predictability (Deterministic vs.

Probabilistic)
First, consider how predictable your process is. Level 2 agents
excel at deterministic tasks—processes that follow clear,
unchanging rules. They’re like reliable workers who execute
the same steps perfectly every time. Level 3 agents, powered by
foundation models, can handle probabilistic tasks that require
understanding context and making judgment calls.
At the marketing agency, Jenny’s team analyzed their reporting
process and found that about 80% of it was deterministic—pulling
specific metrics from various platforms and organizing them in
a standard format. This portion was perfect for a Level 2 agent.
However, 20% involved interpreting trends and writing insights,
which required probabilistic thinking that is better suited for a Level
3 agent.

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2. Error Sensitivity
Next, assess how critical errors would be to your operation.
Level 2 agents are highly reliable for tasks where accuracy is
paramount because they follow exact rules and won’t deviate
from prescribed patterns. They’re ideal for financial calculations,
data processing, and other tasks where errors could be costly.
Level 3 agents, while more flexible, may occasionally produce
unexpected outputs or make creative interpretations. They’re
better suited for tasks where some variation is acceptable or even
beneficial, like content generation or pattern analysis.
For the marketing agency’s client reporting, accuracy in
the data gathering and calculations was crucial—a single error
could damage client trust. This made Level 2 agents perfect for
the data processing portion. However, slight variations in the
written analysis were acceptable and sometimes even desirable,
making this part suitable for a Level 3 agent.

3. Input Variability
Finally, evaluate how much your inputs vary. Level 2 agents
work best with standardized inputs—they need consistent
data formats and predictable scenarios. They struggle with
exceptions and can’t adapt to unexpected variations without
being reprogrammed.
Level 3 agents, however, excel at handling variable inputs.
They can understand context, adapt to different formats, and make
sense of unstructured data. They’re like experienced professionals
who can adjust their approach based on the situation.
The marketing agency dealt with various data sources,
but they followed consistent formats—perfect for a Level 2
agent. However, the context for interpreting this data varied
significantly across different clients and industries, making a
Level 3 agent necessary for the analytical portion.

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Mapping Characteristics to Agent Levels

To summarize how these criteria map to agent levels:

Criteria Level 2 Agents Level 3 Agents (Agentic

(Intelligent Automation) Workflow)

Task Highly deterministic Handles tasks requiring

Predictability processes with clear rules context understanding and
adaptive responses
Error Requires 100% accuracy Accepts some variability
Sensitivity and reliability in outputs, allowing for
probabilistic decisions
Input Works best with Can process variable or
Variability standardized inputs and unstructured inputs
minimal variations
Best For High-volume, repetitive Tasks that involve reasoning,
tasks that follow fixed decision-making, or natural
workflows language understanding
Example Use Data extraction, financial Report writing, customer
Cases calculations, compliance support chatbots, fraud
checks, structured detection, trend analysis
reporting

Table 8.3: Comparison between Level 2 and Level 3 AI agents (Source: ©

Bornet et al.)

Making the Choice: The Marketing Agency’s

Decision
After evaluating these criteria, we helped the marketing agency
make an interesting decision: they would implement a hybrid
approach using both Level 2 and Level 3 agents for different
parts of the process.
The Level 2 agent (what we call Intelligent Automation)
would handle the deterministic portions of the work:

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• Accessing various platforms (Google Analytics, social

media, advertising platforms)
• Extracting standardized data sets
• Performing calculations and creating visualizations
• Generating the basic report structure

The Level 3 agent (an Agentic Workflow) would then:

• Analyze trends in the processed data

• Generate initial insights and recommendations
• Create draft narrative sections of the report

This hybrid approach capitalized on the strengths of each

agent type. The Level 2 agent could reliably handle precise,
deterministic tasks with near-perfect accuracy, while the Level 3
agent was suited for the more nuanced work of data interpretation
and narrative generation.
In the upcoming section, we will outline how to design and
build Level 3 agents. For building Level 2 agents, we recommend
referring to the Intelligent Automation book.136

The Learning Curve

It’s important to note that implementing AI agents is typically
an iterative process. At the marketing agency, they started with
the Level 2 agent handling just the data collection and basic
reporting. Once this was working smoothly and the team was
comfortable with the technology, they introduced the Level 3
agent for content generation.
Jenny reflected, “At first, I wanted to automate everything at
once. But starting with the basic data work and gradually adding

136
Pascal Bornet, Ian Barkin, and Jochen Wirtz, 2020. “Intelligent
Automation: Learn how to harness Artificial Intelligence to boost business
& make our world more human,” https://www.amazon.com/INTELLIGENT-
AUTOMATION-Artificial-Intelligence-business/dp/B08KTDVHHQ

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more sophisticated capabilities helped our team adapt and

actually shaped how we thought about our reporting process.”

Results and Lessons Learned

Six months after implementation, the marketing agency’s AI agent
system was processing reports for all clients with remarkable
efficiency:

• Report generation time reduced to 45 minutes

• Data accuracy maintained at 99.95%
• Client satisfaction scores increased by 15%
• Marketing team reported 60% more time for strategic
work

Jenny reflected on their journey: “The key was not rushing

to automation. By taking a methodical approach to design and
building trust gradually, we created a system that both our team
and our clients trust completely.”
The success of the marketing agency’s reporting system has
opened new possibilities for AI agent implementation across
their organization. They’re now exploring applications in
campaign optimization, content creation, and customer journey
analysis.
The lessons learned from this implementation highlight the
importance of thoughtful design that considers both technical
capabilities and human factors. As we’ll explore in the next
chapter, these design decisions lay the crucial groundwork for
successful implementation and deployment.

Moving Forward
With clear roles defined for both their Level 2 and Level 3
agents, the marketing agency was ready to move into the detailed
design phase of their implementation. The clear delineation of
responsibilities and capabilities would prove crucial for the next

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steps of mapping process flows and establishing performance

criteria.
Remember, the goal isn’t to create the most sophisticated
agent possible but rather to design one that reliably delivers
value while integrating smoothly with your human team’s
workflows. As we’ll explore in the next chapter, this foundation
of clearly defined roles and capabilities is essential for successful
implementation.

Step 3: Designing AI Agents for Success

After identifying the right opportunity and determining the
appropriate type of agent, the next crucial phase is designing
your AI agents for successful implementation. This is where
theory meets practice, and where many organizations either set
themselves up for success or stumble into common pitfalls. Let’s
explore this through our own experience creating an AI agent for
newsletter automation.

The Newsletter Challenge: A Real-World Case

Study
As authors and influencers in the AI space, we found ourselves
spending countless hours each week curating content, writing
summaries, and creating newsletters for our community. It was
a labor-intensive process that, while valuable for our readers,
took time away from deeper research and writing. That’s when
we decided to apply our expertise in AI agents to solve this
challenge.
“We were spending nearly one day each week just on
newsletter creation,” Pascal recalls. “We knew there had to be
a better way.” The result? A new AI agent system that not only
automated much of the process but also helped us grow our new
newsletter to over 300,000 subscribers in just one month.

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In Chapter 9, we discuss how we developed this innovative

agentic business opportunity and transformed it into a venture,
highlighting the immense potential of agentic AI to generate
new business possibilities.

Design Principle #1: Start with the End in Mind

Before diving into technical specifications, successful agent
design begins with clear success criteria. “One of the biggest
mistakes we see,” Rakesh shares from his consulting experience,
“is organizations jumping straight to solution design without
defining what success looks like.”
Defining clear success criteria is essential for three key
reasons. First, it provides clear targets for the development team
and helps prioritize features. Second, it establishes benchmarks
for measuring the return on investment and system performance.
Third, it helps manage stakeholders’ expectations and build
confidence in the new system.
Without clear success criteria, we might have built a system
that was technically impressive but didn’t actually solve our
core business needs.
Success criteria should include both quantitative and
qualitative metrics:

• Quantitative metrics might include efficiency gains, error

reduction rates, or specific performance targets
• Qualitative metrics often focus on user experience,
quality of output, and system reliability

For example, in our newsletter case, we established metrics

like:

• Reducing creation time from 15 to 3 hours per week

• Maintaining 95%+ accuracy in content
• Achieving 40%+ email open rates

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But more importantly, we also defined qualitative success

criteria around content relevance and reader experience.

Design Principle #2: Understand Your Current

State
“Many organizations want to leap straight to the future,” we
observe, “but understanding your current process is crucial for
successful agent design.”
Understanding your current workflow in detail before
designing AI agents is crucial for several reasons. First, it reveals
hidden complexities and dependencies that might not be apparent
at first glance. Second, it helps identify potential bottlenecks
and inefficiencies that the new system should address. Finally,
it ensures that critical business rules and exceptions aren’t
overlooked in the automation process.
When we started mapping our workflow, we discovered
that what we thought was a simple four-step process actually
involved dozens of micro-decisions and exceptions that we
handled automatically. Without documenting these, we would
have built an agentic system that missed crucial nuances.
Here are the issues we faced with the existing process of
building the newsletter:

• Time-Consuming: Manual searching, summarizing, com-

piling, and formatting require significant effort and multiple
hours each day and week. We estimated the total workload
to be about 10 hours per week per newsletter.

• Prone to Errors: We were likely to make mistakes in

repetitive tasks, such as summarizing and formatting.
This happened a few times and was very damaging to
our reputation.

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• Scalability Issues: As the volume of articles grows, the

manual process becomes unsustainable, requiring hours
of work.

• Inconsistent Quality: Output used to vary depending on

the individual performing each task, leading to uneven
results when the team changes.

Design Principle #3: Design the Target Process

After documenting our existing workflow, instead of simply
automating the old process, we redesigned it entirely with
the outcome in mind. Based on our experience, holding onto
outdated workflows often leads to inefficiencies, as automation
alone won’t fix fundamental design flaws.
As Shail usually says, “Garbage in, garbage out. If
you automate a flawed process, it will just result in flawed
automation.”
Instead, we focused on what we wanted to achieve—a stream-
lined, scalable, and error-free newsletter production process—and
built the workflow around that goal. The new process follows a
structured daily and weekly workflow.
Each day, relevant articles should be identified, key insights
summarized, and a formatted email sent for human review. The
selected summaries should then be compiled into a structured
document, ensuring quality control.
On Mondays, the content should be formatted into a visually
polished newsletter, reviewed for accuracy, and finalized for
distribution.
By breaking the process into clear, well-defined steps, we
eliminated unnecessary complexity, improved quality assurance,
and ensured a smooth publishing cycle. The key lesson: don’t
just automate—rethink, redesign, and optimize for the best
possible outcome.

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Design Principle #4: Choose the Right

Architecture
One of the most critical decisions in agent design is the
architectural approach. “Think of it like building a house,” we
explain. “You could try to create one massive room that serves
every purpose, or you could design specialized spaces that work
together harmoniously. In our experience, the latter approach
almost always works better.”
This brings us to a fundamental question in agent design:
Should you build one complex agent to handle everything or
create a team of specialized agents?
When designing our newsletter automation system, we
faced a crucial decision: should we build one complex agent to
handle everything or create a team of specialized agents? Our
experience implementing AI systems for various companies has
taught us that simpler, specialized components often work better
than complex, monolithic ones. At this stage of the book, you
have already heard several times our design principle of “one
tool, one agent.” Let’s explore the key design decisions that led
to our successful design.
“Initially, we were tempted to build one ‘super-agent’ that
could handle everything from content discovery to newsletter
formatting,” Rakesh, one of our co-authors, explains. “But we
quickly realized this would be like asking one person to be
simultaneously a researcher, writer, editor, and designer—it
rarely works well.”
Hence, we designed a comprehensive team of specialized
agents:

Agent Name Agent Role

Search Agent Finds relevant articles on the web
Summarization Agent Summarizes key points from the articles
Email Agent Sends summaries via email every day

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Agent Name Agent Role

Compiler Agent Organizes content based on selected
articles
Newsletter Formatting Agent Prepares final newsletter
Manager Agent Coordinates + final delivery

Table 8.4: Our team of specialized AI agents for the Newsletter automation
(Source: © Bornet et al.)

This modular approach allowed each agent to excel in its

specialized role while maintaining system flexibility through
coordinated interaction.

Key Principles That Guided Our Design:

We followed six core principles in designing our multi-agent
system:

1. Clarity of Purpose: “Each agent needs to be like a skilled

specialist,” Rakesh explains. “Just as you wouldn’t ask
a heart surgeon to also be an anesthesiologist, we gave
each agent a clear, focused role.”

2. Efficiency Through Specialization: Rather than building

complex agents that could do many things adequately, we
created simple agents that could do one thing exceptionally
well.

3. Scalability By Design: “We designed the system of

agents to handle increasing workloads,” Rakesh shares.
For example, if content volume increases, we can
introduce multiple Search Agents working in parallel or
optimize the Summarization Agent for faster processing.

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4. Autonomous Operation: Each agent operates inde-

pendently within its domain, making decisions based on
clear criteria without needing constant oversight.

5. Seamless Collaboration: The agents work together like

a well-coordinated team, with standardized communica-
tion protocols and clear handoff points.

6. Centralized Orchestration: Our Manager Agent

ensures all components work in harmony, much like a
conductor leading an orchestra.

Design Principle #5: Build in Human-AI

Collaboration
Perhaps the most crucial aspect of agent design is determining
how humans and AI will work together. “The goal isn’t to remove
humans from the process,” Nandan emphasizes, “but to create
a symbiotic relationship where both humans and AI contribute
their unique strengths.”
This principle becomes particularly important when we
consider the current limitations of AI agents. At Levels 1-3
of our Progression Framework, agents still lack true adaptive
learning and sophisticated reasoning capabilities. This means
human expertise remains essential for:

1. Strategic Decision-Making: Humans excel at

understanding context and making nuanced judgments
2. Quality Assurance: Providing oversight and catching
subtle errors
3. Exception Handling: Managing unexpected situations
that fall outside normal parameters
4. Continuous Improvement: Identifying opportunities
for system enhancement

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When we first designed our newsletter system, we faced

a fundamental question: Which tasks should be automated,
and which should remain in human hands? Our approach was
to think not in terms of what “could” be automated but what
“should” be automated.
We identified two critical points where human input was
essential:
The first key human touchpoint comes after our AI agents
have gathered and summarized content. A human editor reviews
the daily summaries and selects the most relevant pieces for the
newsletter. This decision requires understanding our audience’s
needs, recognizing emerging trends, and making nuanced
judgments about content value—capabilities that remain uniquely
human.
During implementation, we discovered an interesting pattern:
when we allowed the AI to make final content selections, the
newsletters became technically accurate but somehow soulless.
They lacked the strategic narrative and thematic coherence that
comes from human curation. By keeping humans in this crucial
decision-making role, we maintained the newsletter’s distinctive
voice and strategic relevance.
The second critical human touchpoint occurs just before
publication. Here, human editors review the complete newsletter,
add context where needed, and ensure the content aligns with our
editorial standards. This final review isn’t just about catching
errors—it’s about ensuring the newsletter provides genuine
value to our readers.
“This human-in-the-loop approach,” Nandan explains,
“gives us the best of both worlds. The agents handle the time-
consuming tasks, while humans focus on strategic decisions that
require judgment and industry expertise.”

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The Outcome: The Target Agentic System

After a few days of work, here we are. Below is the new target
process we have designed, presenting the flow of the key
activities, the agents in charge, and the human in the loop:

Daily Workflow
1. Search Agent: Searches the web for relevant articles and
sends links to the Summarization Agent.
2. Summarization Agent: Summarizes each article into
three key points and sends summaries to the Email
Delivery Agent.
3. Email Delivery Agent: Compiles summaries into a
formatted email and sends it to the human reviewer.
4. Human Reviewer: Reviews daily emails and replies
with preferred articles.
5. Compiler Agent: Compiles daily selected summaries
into a structured Google Doc and performs QA.

Weekly Workflow
1. Newsletter Formatting Agent: On Mondays, the
agent formats the Google Doc into a visually appealing
newsletter.
2. Manager Agent: Reviews the formatted newsletter and
sends it to the human reviewer.
3. Human Reviewer: Performs final review and posts the
newsletter to desired platforms.

Step 4: Implementing Your AI Agents

Choosing the Right AI Agent Platform:
Prioritize Speed Over Perfection
The AI agent platform market has evolved rapidly, with
approximately 400 vendors now offering solutions across different

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levels of sophistication and ease of use. This abundance of choice,

while providing numerous options for organizations, can also
lead to decision paralysis. Through our work implementing AI
agents across various industries, we’ve observed that companies
often spend months evaluating platforms while their competitors
forge ahead with implementations and gain valuable market
advantages.
Selecting the right platform requires understanding the three
main categories available in today’s market: full-code, low-
code, and no-code solutions. Each category serves different
organizational needs and comes with its own set of trade-offs
between flexibility, speed of implementation, and required
technical expertise.

No-Code Platforms: Rapid Deployment and

Accessibility
Think of no-code platforms as the “LEGO blocks” of AI agent
development. They provide pre-built components that you can
assemble without any technical expertise. Solutions like Bizway,
Beam, N8N, and Relevance AI enable business users to create
functional AI agents through intuitive interfaces without writing
any code. These platforms are ideal for organizations that need
to quickly implement basic to moderate complexity agents or
those looking to prove the value of AI agents before investing in
more sophisticated solutions.
While no-code platforms might seem limited compared to
their more technical counterparts, they often provide surprisingly
sophisticated capabilities through configuration rather than
coding. For example, Relevance AI offers advanced features like
vector-based processing and sophisticated search capabilities
through a user-friendly interface.

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Full-Code Platforms: Maximum Control

and Customization
On the other extreme side of the spectrum, full-code platforms
provide the highest level of control and flexibility but require
significant development resources. These platforms, such
as LangGraph, CrewAI, and AutoGen, offer comprehensive
frameworks that allow organizations to build highly customized
AI agents. They’re particularly well-suited for enterprises that
need to maintain strict control over their data, require complex
integrations with existing systems, or operate in heavily regulated
industries.
When working with full-code platforms, organizations
can fine-tune every aspect of their AI agents’ behavior, from
decision-making processes to data-handling protocols. This
level of control comes at the cost of longer development cycles
and the need for specialized technical expertise. While platforms
like LangGraph offer extensive documentation and growing
community support, the learning curve remains steep.
These sophisticated agents require careful orchestration of
multiple tools and complex workflow management, which full-
code platforms handle adeptly. However, organizations should
carefully consider whether this level of control is truly necessary
for their use case, as the additional development overhead can
significantly delay implementation and time to value.

Low-Code Platforms: The Balanced Approach

Low-code platforms strike a balance between customization and
accessibility. Platforms like WatsonX Assistant, Agentforce, and
UiPath and Microsoft Copilot’s Agent Builders provide visual
development environments while still allowing for significant
customization through code when needed. These platforms excel
at enabling collaboration between business users and technical
teams, making them particularly effective for organizations that

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need to rapidly deploy AI agents while maintaining some level

of customization.
The low-code approach significantly reduces the time and
technical expertise required for implementation while still
providing enough flexibility for most enterprise use cases.
These platforms typically offer pre-built components and
integrations that can be customized to fit specific business needs.
For instance, ServiceNow’s Virtual Agent platform provides
ready-to-use components for common business processes while
allowing organizations to extend functionality through custom
development when needed.

Making the Selection: Key Considerations

To make a confident and efficient decision, focus on a practical
framework rather than endless feature comparisons. Start by
identifying your top three to four non-negotiables based on your
business priorities. Assign weights to each criterion, considering
factors like ease of use, customization, integration, scalability,
security, and cost. Then, score potential platforms based on
these factors, select the highest-ranking one, and move forward.
Technical expertise availability often serves as a primary
deciding factor. Organizations with strong development teams
might naturally gravitate toward full-code solutions. However,
we’ve observed that even technically capable organizations
sometimes benefit from starting with low-code or no-code
solutions to implement faster and gain practical experience
before moving to more complex implementations.
Another critical consideration is the potential for scaling
agent development across the organization through citizen
development. No-code and low-code platforms enable a
democratized approach where employees throughout the
organization can participate in building and customizing agents.
This democratization of agent development can dramatically
accelerate the impact of AI initiatives and lead to more
innovative solutions, as employees who understand specific

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business challenges can directly contribute to creating solutions.

For instance, a customer service representative might create an
agent to handle routine inquiries, while a finance analyst might
develop an agent to automate report generation. This distributed
approach to agent development can significantly multiply the
value and reach of AI within an organization.
Integration requirements with existing systems and data
sources also play a crucial role in platform selection. While
most platforms offer API connectivity, the ease and depth of
integration vary significantly. Microsoft Copilot’s Agent Builder,
for instance, provides seamless integration with the Microsoft
ecosystem, making it an attractive choice for organizations
heavily invested in Microsoft technologies.
Security and compliance requirements can significantly
influence platform choice, particularly for organizations in
regulated industries. Full-code platforms typically offer the most
control over data handling and security protocols, while no-code
platforms might have limitations in these areas.

Avoid Overthinking
No platform is perfect, and waiting for the “ideal” choice can
lead to lost opportunities. The best strategy is to start small,
experiment, and scale as you learn. Many successful companies
begin with a simple AI agent on a low-code platform, validate
its impact, and gradually migrate to more advanced solutions as
their needs evolve.
While you are still deciding, your competitors are already
deploying AI agents that are transforming their industries. The
businesses that act fast will lead the AI revolution, while those
that hesitate will struggle to catch up. The best AI strategy is not
the perfect one—it’s the one you start today.

***

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In the following sections, we’ve used our experience to create

a universal guide that works across platforms, highlighting the
critical success factors, nuanced decisions, and hidden features
that truly make a difference in building effective AI agents. The
advantage is that you can use it independently of the platform you
choose; however, the drawback is that it is not platform-specific,
and you might notice some variations in the terminology and
details of functionalities. Yet, as an illustration, we have chosen
to provide a detailed step-by-step guide for building an agent on
the low-code platform Relevance AI, which you will find in the
appendices of the book.

Building Effective AI Agents: The A.G.E.N.T.

Framework
From our experience, we’ve learned that success often lies not
in the sophistication of the technology but in the clarity and
thoroughness of how we define and structure our AI agents.
Through numerous implementations and, admittedly, some
painful lessons, we’ve developed what we call the A.G.E.N.T.
framework—a comprehensive approach to building reliable AI
agents that actually deliver value.
The framework consists of five critical components:

• Agent Identity (Who is the agent?)

• Gear & Brain (What powers the agent?)
• Execution & Workflow (How does the agent work?)
• Navigation & Rules (How does the agent make
decisions?)
• Testing & Trust (How do we improve and scale the
agent?)

Think of building an AI agent as hiring and training a new

employee. You wouldn’t bring someone into your organization
without clearly defining their role, establishing their responsibil-

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ities, and setting up the proper workflows, tools, and rules. The
same principle applies to AI agents, but it’s even more important
because these digital workers require extremely precise instruc-
tions to function effectively.
Let’s dive deep into the first crucial component of the
framework: Agent Identity.

A – Agent Identity: Who is the Agent?

The single most important step when building an AI agent is
defining its identity—its purpose, role, and operational scope.
This is where most people make their first mistake. They rush
ahead, eager to see their AI in action, without taking the time to
carefully define what the agent is supposed to do. The result? An
AI that behaves unpredictably, produces inconsistent results, or
simply fails to deliver value.
To illustrate why this step is so crucial, imagine hiring a new
employee. Would you bring someone into your company and
say, “Just help out however you think best”? Of course not. A
new hire needs clear guidance: their role, their responsibilities,
and their limitations. The same applies to AI agents. If you
don’t give them a structured identity, they’ll wander aimlessly,
often producing results that are unreliable, irrelevant, or even
counterproductive.

Why Defining an Agent’s Identity is Critical

AI agents are not intuitive thinkers. They don’t have gut feelings
or common sense. They operate within the constraints you set
for them. If you fail to define their identity properly, you will see
the consequences in three ways:

1. Unreliable Behavior—The agent may respond in ways

that don’t align with your goals because it lacks direction.

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2. Inconsistency—The same question may yield different

results each time because the agent doesn’t have clear
guidelines.
3. Lack of Control—If an agent’s scope isn’t clearly
defined, it might produce unnecessary, irrelevant, or
even harmful outputs.

To see this in action, consider our journey in building an AI-

powered summarization assistant for our newsletter. Initially, we
gave the agent a simple directive:
“Summarize news articles and research papers.”
Sounds reasonable, right? But the results were a mess. The
AI pulled random articles, summarized them with no clear
structure, and sometimes even included outdated or irrelevant
sources. It wasn’t the AI’s fault—it was just following vague
instructions.
So, we refined the agent’s identity and gave it a more precise
role:
“You are a summarization assistant specializing in AI and
business trends. Your job is to take news articles and research
papers from sources like MIT Tech Review, arXiv, and Harvard
Business Review and distill them into clear, engaging summaries.
Each summary must be under 150 words, capture key insights, and
maintain the original meaning. The tone should be professional
yet accessible, aligning with our newsletter’s style.”
With this level of clarity, our AI went from an unreliable
content aggregator to a laser-focused research partner. It now
delivers concise, high-value summaries that keep our readers
ahead of the curve without us having to sift through mountains
of information. You can refer to the appendix, where we provide
you with this agent’s detailed identity. You can use it as a
template for writing your own.

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Building a Strong AI Agent: Think Like a Manager

To put this all into perspective, imagine managing a remote
employee who can’t think independently—they only follow
instructions. You’d need to:

1. Give them a clear job description

2. Tell them exactly what tasks they should (and shouldn’t)
do
3. Define how they should communicate
4. Set boundaries and escalation points

Now, replace that employee with your AI agent. If you don’t

define these things, your AI will be unreliable. But if you do,
you’ll have an agent that performs consistently, efficiently, and
intelligently within its domain.
Before you move forward to choosing models and workflows,
take the time to write down your agent’s identity. The better this
foundation, the more powerful and effective your AI agent will
be.

The Key Elements of a Strong Agent Identity

Defining an AI agent’s identity is more than just stating its
function. It requires precision, just like writing a well-crafted
job description.

1. Purpose: What is the Agent’s Primary Mission?

Every AI agent should have a clear mission statement that
answers the question: Why does this agent exist? A strong
purpose statement leaves no room for ambiguity.

• Weak Purpose: “Help with customer support.”

• Strong Purpose: “Assist customers by resolving com-
mon technical issues with step-by-step solutions in a
professional, friendly manner.”

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The stronger the purpose, the more aligned the agent’s

outputs will be with user expectations.

2. Role: What Persona Does the Agent Assume?

An agent’s role defines how it interacts with users and the
expertise it simulates—essentially, its professional identity. A
well-defined role ensures users instantly grasp its capabilities
and limitations, while also providing clear boundaries for what
the agent should and shouldn’t do.
For example, a financial AI agent might be a:

• “Financial Analyst” (providing investment insights and

risk assessments)
• “Personal Finance Coach” (helping users budget and
save money)
• “Tax Consultant” (answering tax-related questions with
compliance in mind)

Each of these roles will require the AI to communicate

differently and focus on different kinds of information.

3. Scope: What Can the Agent Do, and What Should

It Avoid?
Without clear boundaries, an AI agent may drift beyond its
intended function. Setting well-defined scope constraints ensures
it stays focused.
For example, a customer service agent might be restricted to:

• Answering FAQs based on company knowledge

• Providing troubleshooting steps for common problems
• Escalating complex issues to human representatives

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It should not attempt to:

• Provide legal or medical advice

• Make unauthorized decisions
• Generate speculative responses

Boundaries prevent the agent from venturing into areas

where mistakes could be costly.

G – Gear & Brain: Powering Your AI Agent

Once your AI agent has a clearly defined identity, the next step
is equipping it with the right gear and brain—the tools, models,
and knowledge it needs to function effectively. This is where
many builders go wrong. They either overcomplicate things by
choosing advanced setups they don’t need or underpower their
agents by selecting the wrong tools for the job.
Think of this step as assembling a high-performance vehicle.
You wouldn’t put a racing engine in a delivery van or expect a
family sedan to win Formula 1. The same logic applies to AI
agents—choosing the wrong combination of AI models, tools,
and data sources will lead to inefficiency, poor performance, or
outright failure.

1. Selecting the Right AI Model: Balancing Power,

Cost, and Efficiency
Most platforms let you choose which AI model (or combination
of models) your agent will use. This decision is critical—it
determines how your agent thinks, responds, and processes
information. The AI model is the brain of the agent, dictating
how well it understands, reasons, and generates responses. There
are two key trade-offs to consider: model size and reasoning
capability.
Smaller models, such as Mini, Phi, or Flash, are fast, efficient,
and cost-effective, making them ideal for simple queries or

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predefined tasks. However, they struggle with deep reasoning

and complex problem-solving. On the other hand, larger
models like GPT, Claude Opus, or Gemini Ultra can analyze
nuanced information, generate high-quality responses, and think
critically—but they consume more resources and cost more to
operate.
For cost-sensitive applications, using a small model for
routine tasks and reserving a larger model for complex queries
is often the best approach. Open-source models like Mistral or
Llama 70B provide more control and lower costs but require in-
house expertise to fine-tune and deploy.

Temperature settings
Another important factor in tuning your AI agent’s performance
is adjusting the temperature settings, a feature available on many
platforms. The temperature setting controls how deterministic or
creative the AI’s responses will be, impacting both the agent’s
reliability and flexibility.
A low temperature (e.g., 0 to 0.3) makes the AI more
predictable and consistent, sticking closely to factual answers
and minimizing randomness. This is ideal for applications where
accuracy is crucial, such as summarization agents for newsletters,
research assistants, or legal AI tools. You want the AI to generate
concise, fact-based summaries rather than producing unexpected
variations in tone or content.
A high temperature (e.g., 0.7 to 1.0) makes the AI more
creative and open-ended, introducing variety in phrasing and
responses. This setting is useful for brainstorming, generating
marketing copy, or writing creative content, where originality
is more important than strict accuracy. However, in structured
environments like a summarization agent, a high temperature
can lead to inconsistencies, hallucinations, or overly wordy
outputs that stray from the original meaning.

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For deeper insights into AI model selection for AI agents,

resources like Hugging Face model comparisons for agents137
and AI research papers from OpenAI, Anthropic, or DeepMind
can provide valuable benchmarks.138

2. Choosing the Right Tools: Precision Over Power

An AI agent isn’t a text generator—it must interact with external
systems, retrieve real-time data, and take action. Just as a
company wouldn’t give an employee unrestricted access to its
systems, an AI agent’s toolset must be clearly defined, limited,
and monitored.
During our newsletter project, we initially gave our Search
Agent unrestricted access to web scraping tools. The result
was a flood of content from unreliable sources and occasional
server blocks from overwhelmed websites. We learned the
importance of defining each tool’s purpose, limitations, and
usage parameters in detail. For our Search Agent, this meant
specifying not just which APIs it can use, but exactly how. You
can refer to the appendix, where we provide you with the detailed
tool specifications of our agent. You can use it as a template for
writing your own.
APIs are the most common tools, allowing agents to fetch
real-time information or execute actions. A financial agent,
for example, might use a market data API to pull stock prices,
while a scheduling assistant relies on a calendar API. However,
unrestricted API access can lead to rate limits, security
vulnerabilities, or legal issues—as was the case with many web-
scraping agents that ended up blocked or banned for excessive
crawling.

137
Galileo AI, n.d. “Agent Leaderboard,” https://huggingface.co/spaces/
galileo-ai/agent-leaderboard
138
Karthik Narasimhan, 2024. “Benchmarking AI Agents,” https://sierra.ai/
blog/benchmarking-ai-agents

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To prevent misuse, agents need structured usage policies

that define clear operational boundaries. Several key parameters
should be specified:

• Rate Limits & Cost Control—To ensure efficiency and

cost control, the agent must operate within a set request
limit per minute. Many APIs charge per request, meaning
uncontrolled usage can lead to high costs. Additionally,
exceeding provider-imposed limits can trigger throttling,
where the API slows down or temporarily blocks requests
to prevent system overload. By managing request
frequency, the agent avoids unnecessary expenses while
maintaining smooth and uninterrupted performance.

• Source Reliability—The agent should only pull data from

pre-approved, credible sources to prevent misinformation.
This might include whitelisting trusted news outlets or
filtering content based on quality standards. For example,
our research agent was programmed to pull information
only from pre-approved, reputable sources such as MIT
Tech Review, arXiv, or Harvard Business Review.

• Secure API Access—API keys should be rotated

regularly and protected with authentication mechanisms
(e.g., token-based access) to prevent unauthorized use.
They should never be hardcoded into scripts.

• Circuit Breakers—Stop execution if repeated failures

occur, rather than continuing to send faulty requests.
For example, if an API fails three times in a row, the
agent should pause and notify an administrator instead
of retrying indefinitely.

• Fallback Systems—Ensure continuity by switching

to a backup process when something goes wrong. For

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example, if a news API is down, the AI can pull headlines

from a secondary source instead of returning an error.

Without these safety nets, an AI agent can overload itself,

frustrate users, and even damage business operations. For those
looking to explore AI tool integrations further, you can refer to
Part 2 of this book. You can also refer to API documentation from
OpenAI,139 Google,140 and AWS,141 which provides excellent
guidance on responsible implementation.

Selecting Knowledge Sources: The Foundation of

Reliable AI Agent
An AI agent must retrieve and process information from reliable
sources. If these sources are poorly defined, the agent may
hallucinate, pull incorrect data, or provide misleading responses.
There are three primary ways to structure an agent’s knowledge
base: databases, APIs, and document embeddings.
A database is best for internal knowledge—such as customer
histories, policies, or proprietary research. APIs, on the other hand,
ensure the agent can fetch live data, such as weather forecasts,
legal updates, or financial statistics. Meanwhile, document
embeddings allow AI to search and retrieve information from
large collections of text, making them ideal for legal, academic,
and enterprise AI solutions.
However, not all knowledge should be included. If the agent
has access to outdated or biased sources, it risks spreading
misinformation. Similarly, unfiltered open-web search can

139
OpenAI, n.d. “Production Best Practices,” https://platform.openai.com/
docs/guides/production-best-practices
140
Google, n.d. “APIs and reference,” https://cloud.google.com/generative-
ai-app-builder/docs/apis
141
Maira Ladeira Tanke, et al., 2024. “Best practices for building robust
generative AI applications with Amazon Bedrock Agents – Part 1,” https://
aws.amazon.com/blogs/machine-learning/best-practices-for-building-
robust-generative-ai-applications-with-amazon-bedrock-agents-part-1/

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be dangerous, as it may lead to the retrieval of low-quality or

misleading content. Instead, AI agents should be trained on
trusted, domain-specific datasets and restricted from generating
speculative information.
Those interested in refining knowledge selection strategies
can refer to Part 2 of this book. You can also refer to vector
database platforms like Weaviate142 or Pinecone,143 as well as
academic research from Stanford AI Lab,144 which provide
advanced insights.

Final Recommendations: Building AI That Works

Smarter, Not Harder
Based on our experience, the best AI agents aren’t the ones
with the most power, but the ones with the right balance of
intelligence, tools, and knowledge. Keeping models efficient,
restricting tool access, and curating high-quality knowledge
sources ensures reliability, security, and trustworthiness.
Poorly configured AI is wasteful at best and dangerous at
worst. The key is to start with clear definitions, strict parameters,
and reliable sources, ensuring the agent operates within
controlled and predictable boundaries.

E – Execution & Workflow (How Does the Agent

Work?)
Once your AI agent has a clearly defined identity and is equipped
with the right gear and brain, it’s time to focus on execution—
how the agent actually operates in real-world conditions.
Many AI projects fail not because of bad models or missing
tools, but because their workflows are poorly designed. Without
a clear structure, it risks acting unpredictably, handling inputs

142
Weaviate, 2025. https://weaviate.io
143
Pinecone, 2025. https://www.pinecone.io/product/
144
Stanford Autonomous Agents Lab, 2025. https://www.autonomousagents.
stanford.edu

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incorrectly, or responding inconsistently. Just like a well-trained

employee follows a structured work process, an AI agent needs
a defined input format, a logical workflow, and clear triggers that
dictate when and how it operates.

Defining Input & Output: Speaking the Right

Language
Before an AI agent can function properly, it must understand the
format of the data it receives and produces.
The precise definition of inputs and outputs might seem
obvious, but it’s actually one of the most crucial aspects of
agent design. We learned this lesson with our newsletter system.
Initially, we had loosely defined how our Summarization
Agent should receive content from the Search Agent. The
result? Constant errors from mismatched data formats, missing
metadata, and inconsistent content structures.
Consider what happened when we didn’t properly specify
the input format for article URLs. Some came with query
parameters, others without; some included anchor tags, and
others were clean URLs. This seemingly minor oversight caused
our system to sometimes process the same article multiple times
or miss articles entirely. By defining exact input specifications—
including URL formatting, required metadata fields, and content
structure—we eliminated these issues entirely.
The same principle applies to outputs. When we first
launched, our Summarization Agent would sometimes produce
summaries that were too long for our email template, forcing last-
minute manual editing. By specifying exact output parameters—
character limits, required sections, and formatting rules—we
ensured that each agent’s output seamlessly fed into the next stage
of the process.
To prevent such issues, every AI agent should have:

• Strict input validation: Ensure the agent can process only

the formats it’s designed for.

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• Clear output definitions: The receiving system should

always know what to expect.

You can refer to the appendix, where we provide you with

such detailed examples.
For those dealing with high-volume or critical data process-
ing, frameworks like OpenAPI for structured data handling145
or JSON Schema for validation can help standardize inputs and
outputs across AI workflows.146

Designing the Workflow: Structuring the AI’s

Decision-Making Process
An AI agent doesn’t operate in isolation—it is activated and
then follows a sequence of steps to process information and
make decisions. A properly defined workflow specifies not just
the steps, but the transitions between them. Without a defined
workflow, it may loop unnecessarily, execute actions at the
wrong time, or produce inconsistent behavior.
A well-structured workflow includes:

1. Activation Criteria: When does the agent start working?

Does it wait for user input, an API call, or a scheduled
task? There are three primary types of triggers:
• User Input: The agent responds only when prompted
(e.g., chatbots, virtual assistants).
• API Calls: These activate when another system
requests data (e.g., AI-driven automation).
• Scheduled Execution: It runs at fixed intervals (e.g.,
daily reports, background data processing).

145
Michelle Pokrass, 2024. “Introducing Structured Outputs in the API,”
https://openai.com/index/introducing-structured-outputs-in-the-api/
146
Stephen Collins, 2024. “Introducing JSON Schemas for AI Data
Integrity,” https://stephencollins.tech/posts/introducing-json-schemas-for-ai-
data-integrity

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2. Processing Steps: What sequence of actions does the

agent take? For example, does it fetch data first, analyze
it, then generate a response? Does it need to verify
information before taking action?

For example, our Summarization Agent for our newsletter

follows this workflow:

1. Activation Criteria: A new batch of articles and research

papers is uploaded or retrieved from sources like MIT
Tech Review and arXiv research papers by the Research
Agent.
2. Processing Steps: The agent processes the content,
extracting key insights and filtering out irrelevant or
duplicate information.

A well-defined workflow prevents inefficiencies and ensures

the agent operates logically and predictably.
To build a robust, scalable AI, structuring execution
carefully is just as important as selecting the right model. For
deeper insights, developers can explore API best practices from
OpenAI,147 workflow automation strategies from BPMN,148 and
event-driven architectures used in cloud computing frameworks
like AWS Lambda149 and Google Cloud Functions.150

147
OpenAI, n.d. “Safety Best Practices,” https://platform.openai.com/docs/
guides/safety-best-practices
148
Camunda, n.d. “BPMN Workflow Engine,” https://camunda.com/
platform-7/workflow-engine/
149
James Beswick, 2024. “Operating Lambda: Understanding event-driven
architecture – Part 1,” https://aws.amazon.com/blogs/compute/operating-
lambda-understanding-event-driven-architecture-part-1/
150
Observe, n.d. “GCP Cloud Functions,” https://docs.observeinc.com/en/
latest/content/integrations/gcp/cloud-functions.html

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Implementing Fail-Safes: Ensuring AI Reliability

Under Stress
No AI system operates flawlessly. Failures—whether due to
malfunctioning agents, degraded performance, or external
disruptions—are inevitable. The real challenge is not avoiding
failures but managing them effectively to minimize damage and
maintain service continuity.
By implementing circuit breakers and structured error
handling, AI agents can detect issues early, prevent cascading
failures, and ensure graceful recovery. These fail-safes act as the
system’s immune system, protecting both performance and user
trust.

1. Error Handling and Recovery: Keeping AI

Functional
AI must be designed to recover intelligently. Instead of failing
outright, the best systems use a three-step recovery approach:

1. Automated Recovery: The system automatically retries

failed processes, waiting longer between each attempt to
avoid overload while increasing the chances of success.

2. Graceful Degradation: If a function fails repeatedly, the

AI reduces complexity instead of stopping entirely. For
example, if a summarization agent encounters errors, it
can still forward article links and metadata rather than
fail completely.

3. Human Escalation: If the AI can’t resolve an issue with

confidence, it flags a human, providing relevant context
and suggested resolutions rather than simply failing.

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This structured approach ensures that AI failures remain

controlled, users still receive useful outputs, and critical services
continue running even under stress.

2. Circuit Breakers: Preventing System-Wide Failures

If the errors persist, circuit breakers act as an AI’s immune
system, stopping faulty processes before they cause widespread
damage. Without them, agents can loop on errors, process
unreliable data, or degrade in quality without intervention.
We learned this the hard way when a malfunctioning
summarization agent produced gibberish for two hours, a
compromised news source injected misleading content, and
system overload during a major event resulted in poor-quality
summaries. To prevent these issues, we set quality thresholds—
if a summarization agent produces gibberish three times in a
minute, it is paused automatically and escalated to a human by
sending a message.
During a major tech conference, our system became
overwhelmed, degrading content quality. Circuit breakers now
detect and block low-quality outputs before they reach users,
preventing reputational damage. Platforms like AutoGen and
CrewAI offer built-in safeguards that can be configured to stop
execution when anomalies occur.

3. Escalation to a Human: Knowing When AI Should

Step Back
Some decisions are too complex or high-risk for AI to handle
alone. In these cases, escalating to a human is critical to
maintaining accuracy, compliance, and trust. Instead of blindly
handing off the problem, a well-designed AI should provide
structured escalation.
For example, in the summarization agent for the newsletter,
escalation occurs when the AI detects conflicting or low-
confidence summaries. If multiple sources report the same event

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but provide contradictory details—such as differing figures on

a company’s earnings or inconsistent claims about a product
launch—the summarization agent does not attempt to “guess”
the truth. Instead, it flags the issue to a human editor, providing:

1. The original source articles that contributed to the

summary.
2. A breakdown of inconsistencies detected across sources.
3. A confidence score indicating the reliability of the AI-
generated summary.

This ensures that the editor has all the necessary context to
make an informed decision rather than starting from scratch.
Similarly, if the AI fails to generate a coherent summary—
perhaps due to excessive jargon, AI-generated content pollution,
or poor-quality writing—it escalates to a human with suggested
alternative sources or a request for manual intervention.
For readers who want to explore these topics further,
resources such as Microsoft’s AI Trust and Safety Principles,151
or OpenAI’s API Best Practices152 provide in-depth insights
into fail-safes, error handling, circuit breakers, and human-AI
escalation strategies.

N – Navigation & Rules: How the AI Agent Makes

Decisions
Now that our AI agent has a clear identity, the right tools,
and a structured workflow, we must address one of the most
overlooked but critical aspects of AI design: how the agent makes

151
Microsoft, 2022. “Microsoft Responsible AI Standard v2: General
Requirements,” https://blogs.microsoft.com/wp-content/uploads/prod/sites/
5/2022/06/Microsoft-Responsible-AI-Standard-v2-General-Requirements-3.
pdf
152
OpenAI, n.d. “Safety Best Practices,” https://platform.openai.com/docs/
guides/safety-best-practices

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decisions. An AI agent is only as good as its decision-making

framework. Without well-defined navigation rules, the agent
becomes unpredictable, inconsistent, and—most dangerously—
uncontrollable.
Think of an AI agent as an autonomous vehicle. You wouldn’t
just tell a self-driving car, “Drive to the destination” and expect
it to figure everything out on its own. It needs rules to navigate:

• Which routes are allowed?

• How should it handle obstacles?
• What happens if the road is blocked?

AI agents need similar rules to function reliably. Without

these rules, they make arbitrary choices—or worse, incorrect
ones.

Defining Processing Rules: Filtering, Prioritization,

and Decision Logic
Every AI agent must decide what information matters, what to
ignore, and how to prioritize tasks. Without rules, it may get
bogged down by irrelevant data, return inconsistent results, or
make poor recommendations.
A well-structured processing system includes:

1. Filtering Mechanisms: The agent must distinguish

between relevant and irrelevant data. A research AI, for
example, should prioritize peer-reviewed studies over
unverified blog posts.

2. Prioritization Logic: Some tasks are more urgent or

valuable than others. A customer support AI should
escalate urgent complaints before handling general
inquiries.

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3. Processing Limits: Overloading an agent with too much

data at once slows it down and increases costs. If an AI
retrieves thousands of articles, it should pre-filter based
on key parameters rather than analyzing everything.

When we developed our Search Agent, we initially focused

only on keyword matching and recency. The result? We got
technically relevant but often superficial content that didn’t
provide real value to our readers.
By expanding our processing rules to include sophisticated
relevance scoring, source credibility assessment, and content
diversity requirements, we transformed the agent from a simple
search tool into a discerning content curator. Each rule serves
a specific purpose: relevance scoring ensures content value,
credibility assessment maintains quality standards, and diversity
requirements prevent echo chamber effects.
Failing to define these rules can lead to inefficiencies, bias,
or even incorrect decision-making. To dive deeper into the topic,
you can refer to information retrieval methodologies,153 decision
tree frameworks,154 and ranking algorithms used in machine
learning models to refine these rules.155

Ensuring Transparency: Creating Decision Trails for

AI Accountability
AI decisions should never feel like a black box—users need
to understand why the agent responded a certain way. If an AI
provides a financial recommendation or denies a request, there
must be traceable reasoning behind the decision.

153
Glean, 2024. “A Comprehensive Guide to Information Retrieval in 2024,”
https://www.glean.com/blog/glean-information-retrieval-2024
154
Wikipedia contributors, 2025. “Decision tree learning,” https://
en.wikipedia.org/wiki/Decision_tree_learning
155
Mage, 2024. “Machine Learning (ML) Applications: Ranking,” https://
dev.to/mage_ai/machine-learning-ml-applications-ranking-238d

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One of the best ways to achieve this is by implementing

Decision Trails—logs that record and explain the AI’s thought
process. These logs should:

• Capture input parameters: What data influenced the AI’s

decision?
• Show processing steps: How did it rank options, filter
information, or apply logic?
• Provide justifications: Why did it choose one option over
another?

For example, if our newsletter research agent selects a

research paper to be summarized, its log should include:

• The sources it pulled from (e.g., MIT Tech Review,

arXiv, Harvard Business Review) and the date the article
was written.
• The reasoning behind its selection, such as relevance
to the current newsletter topic and belonging to the
whitelisted sources.
• Any alternative articles it considered but discarded,
along with the rationale.

By maintaining decision transparency, the summarization

agent ensures editorial oversight, builds trust with readers, and
allows for refinement in its selection process—transforming
it from a simple automation tool into a reliable, accountable
research partner.
Most AI agent platforms provide built-in hooks or middle-
ware for logging, which can be customized to generate decision
trails for transparency. These logs can be accessed via dash-
boards for review or debugging. The key is to integrate logging
at key points in the workflow where agents perform tasks. For

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each agent (e.g., Search, Summarization), capture input data

(e.g., keywords, articles), the actions taken (e.g., summarization
method, relevance score), and outputs (e.g., selected articles,
summaries).
AI systems in finance, healthcare, and government are
already being regulated for transparency—meaning clear
decision trails will soon be a requirement, not just a best practice.
To learn more about the topics of logging and transparency, you
can explore Explainable AI (XAI) frameworks,156 compliance
regulations like GDPR’s AI transparency requirements,157 and
audit logging158 practices in enterprise AI.159

T – Testing & Trust: How to Improve and Scale an

AI Agent
Building an AI agent is not a one-time task—it’s an ongoing
process of testing, refining, and scaling. Even the most
well-designed agent will encounter unexpected behaviors,
performance issues, and limitations when deployed in real-
world scenarios. Without a structured improvement cycle, the
agent may produce unreliable results, frustrate users, or fail to
scale effectively. Just like an employee undergoes performance
reviews and training, an AI agent must be continuously tested,
monitored, and optimized.

156
Aman Anand Rai, 2023. “6 Explainable AI (XAI) Frameworks for
Transparency in AI,” https://dev.to/amananandrai/6-explainable-ai-xai-
frameworks-for-transparency-in-ai-3koj
157
EUAIACT contributors, 2025. “Key Issues Transparency Obligations,”
https://www.euaiact.com/key-issue/5
158
API7.ai, 2024. “What’s New in API7 Enterprise 3.2.2: Audit Logging,”
https://api7.ai/blog/api7-3.2.2-audit-logging
159
Vrushank Vyas, 2025. “Beyond Implementation: Why Audit Logs Are
Critical for Enterprise AI Governance,” https://portkey.ai/blog/beyond-
implementation-why-audit-logs-are-critical-for-enterprise-ai-governance/

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Simulating Real-World Use Cases: Ensuring the AI

Works Beyond the Lab
An AI agent may perform well in controlled environments, but real-
world users bring unpredictable inputs, edge cases, and challenges.
The only way to ensure reliability and accuracy is by rigorously
testing the agent in diverse scenarios before deployment.
A well-rounded testing approach should include:

• Common Scenarios: Does the agent handle standard user

requests as expected?
• Edge Cases: How does it respond to ambiguous, poorly
worded, or conflicting inputs?
• Failure Simulations: What happens if an API is down or
if the user provides incomplete information?

For example, a customer support AI must be tested not just

for basic inquiries, but also for cases where a frustrated customer
provides vague, emotional, or misleading input. Similarly, an
AI-powered medical assistant should be stress-tested to detect
and prevent inaccurate medical advice.
Skipping this step can lead to AI hallucinations, inappropriate
responses, or incorrect decision-making, especially when
users interact in ways that weren’t originally anticipated. For
structured testing methods, you can explore LLM evaluation
tools like LangChain’s testing suite,160 AI model benchmarking
from Hugging Face,161 and adversarial testing frameworks from
OpenAI.162

160
Langchain contributors, 2025. «Testing,» https://python.langchain.com/
docs/concepts/testing/
161
Aaditya Ura, Pasquale Minervini, Clémentine Fourrier, 2024. “The Open
Medical-LLM Leaderboard: Benchmarking Large Language Models in
Healthcare,” https://huggingface.co/blog/leaderboard-medicalllm
162
Lama Ahmad et al., 2024. “OpenAI’s Approach to External Red Teaming,”
https://cdn.openai.com/papers/openais-approach-to-external-red-teaming.
pdf

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Collecting Feedback & Monitoring Logs: Learning

from Users and Mistakes
Once an AI agent is live, constant monitoring and user feedback
collection are critical to refining its performance. Even with
well-structured workflows, AI outputs may still deviate, produce
unexpected responses, or fail to meet user expectations.
Effective monitoring includes:

• User Feedback Mechanisms: Allow users to rate

responses, flag incorrect answers, and provide contextual
feedback.
• Log Analysis: Record inputs, outputs, and decision paths
to detect recurring errors or inefficiencies.
• Behavior Tracking: Identify whether users abandon
interactions, request clarifications, or get stuck frequently.

For instance, if an AI-powered recruiting assistant repeatedly

ranks unqualified candidates highly, reviewing its decision
logs can help pinpoint biases or issues in scoring mechanisms.
Similarly, if an e-commerce AI suggests irrelevant products, user
feedback can indicate whether it’s misinterpreting preferences
or over-prioritizing certain trends.
Ignoring this step can result in stagnant AI performance,
user frustration, and lost trust. For example, you can leverage
observability tools like LangSmith,163 and OpenTelemetry for
tracking model behavior and user interactions.164

163
Langchain contributors, 2025. «LangSmith,» https://www.langchain.
com/langsmith
164
Drew Robbins, Liudmila Molkova, 2024. “OpenTelemetry for Generative
AI,” https://opentelemetry.io/blog/2024/otel-generative-ai/

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Refining & Improving: Fine-Tuning the AI for Better

Results
No AI agent is perfect at launch. Performance refinement is
an iterative process that involves adjusting model parameters,
updating prompts, and optimizing workflows based on real-
world results.
Key areas to optimize include:

• Temperature Adjustments: Lower values make responses

more deterministic, while higher values encourage
creativity but increase randomness.
• Prompt Engineering: Modifying instructions, constraints,
and system messages can drastically improve response
accuracy and consistency.
• Workflow Tweaks: If certain tasks are causing delays,
errors, or inefficiencies, adjusting how data is processed
or retrieved can enhance performance.

For instance, an AI-powered legal assistant that struggles

with concise answers might need prompt refinements to enforce
brevity. Meanwhile, an AI that hallucinates too often might
benefit from lower temperature settings and stricter knowledge
retrieval mechanisms.

The Progressive Trust Model

Trust between humans and AI agents isn’t automatic—it must be
earned. We have designed the Progressive Trust Model to reflect
this reality, ensuring that AI systems gradually gain autonomy as
they demonstrate reliability, accuracy, and transparency. Instead
of a binary approach where AI is either fully controlled or fully
independent, this model transitions trust in stages, balancing
efficiency with oversight.

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• Stage 1: High Oversight. During the first month of

operation, human editors reviewed every AI action
in detail. This intensive oversight period served two
purposes: it ensured quality while helping editors
understand the AI’s capabilities and limitations.

• Stage 2: Selective Review. As the system proved its

reliability, we shifted to a more selective review process.
Editors focused their attention on complex cases and
strategic decisions, while routine tasks were handled
more autonomously by the AI.

• Stage 3: Strategic Oversight. In our current operation,

human involvement focuses primarily on strategic
direction and exceptional cases. The AI handles routine
operations with high autonomy, but always within clearly
defined parameters.

This progressive model allows organizations to build

confidence in their AI agents while maintaining appropriate
safeguards. The key insight is that human-AI collaboration isn’t a
binary choice but a spectrum that evolves based on demonstrated
performance. For every agent implementation, we now map out
this progression from high oversight to strategic oversight, with
clear criteria for advancing between stages.
As one client noted, “Starting with high oversight wasn’t
about lack of trust in the technology—it was about giving our
team time to adapt while ensuring business continuity.” This
approach has significantly improved adoption rates and long-
term success of our agent implementations.

Planning for Scaling: Ensuring the Agent Can Handle

Growth
A well-designed AI agent should be able to grow with demand—
handling more users, larger datasets, and increased complexity

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without degradation in performance. Many AI projects fail not

because they don’t work, but because they can’t scale efficiently.
Scalability planning involves:

• Load Testing: Can the agent handle 10x more users

without slowing down?
• Parallel Processing: Can it distribute tasks across
multiple servers or models?
• Cost Optimization: Is the infrastructure cost-effective
at scale, or does performance come at an unsustainable
price?

For example, an AI customer support bot handling 1,000

queries per day might perform well, but if it scales to 100,000
daily interactions, it could slow down or become too expensive
to operate. Ensuring it can distribute workload effectively—
through serverless architectures, caching strategies, and multi-
agent coordination—is key to long-term viability.
Those interested in AI scaling strategies can explore AWS
Auto Scaling,165 Google Cloud AI infrastructure best practices,166
and NVIDIA’s AI deployment frameworks for handling large-
scale inference workloads.167

The Power of Simplicity

One crucial lesson we learned about reliability was the value
of simplicity. Early versions of our newsletter agentic system
included complex recovery procedures and elaborate fallback

165
Amazon Web Services, 2025. https://aws.amazon.com/autoscaling/
features/
166
Google Cloud, 2025. «AI Infrastructure,» https://cloud.google.com/ai-
infrastructure?hl=en
167
NVIDIA, 2025. “Installing AI and Data Science Applications and
Frameworks,” https://docs.nvidia.com/ai-enterprise/deployment/bare-metal/
latest/installing-ai.html

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mechanisms. Over time, we found that simpler, well-tested

processes were often more reliable than sophisticated ones.
“The most reliable systems,” Rakesh often says, “are those
that have fewer things that can go wrong.” This principle guided
us to streamline our processes and eliminate unnecessary
complexity wherever possible.

Using LLM Chatbots to Build Comprehensive

Specifications
We’ve found that AI Chatbots like ChatGPT, Gemini, or
Claude are invaluable in developing these detailed definitions
and parameters, whether it is to define in detail the identity of
an agent, define the right API parameters, or design fallback
strategies. However, the key is knowing how to prompt them
effectively. We like to use a method we call “Progressive
Definition Refinement”:
First, we ask the LLM to generate a basic role description.
Then, we progressively probe for potential issues, edge cases,
and failure modes. For example:

• Initial prompt: “What should a content summarization

agent consider when processing news articles?”
• Follow-up prompts: “What could go wrong with each
of these considerations?” “How should the agent handle
each type of failure?” “What metrics would indicate the
agent is performing well?”

Each response helps us build a more comprehensive

definition, which we then validate against real-world scenarios.

Summary of the Agent Framework

The A.G.E.N.T. framework provides a structured methodology
for building AI agents that are reliable, scalable, and effective.

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Below is a matrix summarizing the key components, making it

easy to implement and apply.

Component Key Question Key Elements Actionable Steps

A – Agent Who is the Purpose, Define a clear mission
Identity agent? Role, Scope statement. Specify
boundaries and
responsibilities. Ensure
alignment with the
intended use.
G – Gear & What powers AI Model, Choose the right
Brain the agent? Tools, model based on
Knowledge performance and cost.
Sources Implement necessary
APIs and tools.
Curate high-quality
knowledge sources.
E – Execution How does the Input/Output, Standardize input/
& Workflow agent work? Workflow output formats. Define
Design, structured workflows.
Triggers & Set activation
Automation triggers and automate
processes.
N – Navigation How does the Processing Establish filtering and
& Rules agent make Rules, Safety prioritization rules.
decisions? Mechanisms, Implement rate limits,
Transparency circuit breakers, and
human escalation
pathways. Maintain
decision logs for
traceability.
T – Testing & How do we Real-World Simulate real-world
Trust improve and Testing, use cases. Collect user
scale the Feedback feedback and monitor
agent? Monitoring, logs. Optimize
Scalability workflows and plan
for scaling.

Table 8.5: Summary of the Agent Framework (Source: © Bornet et al.)

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The Outcome of our Newsletter Agentic Project

The introduction of the agent system has completely transformed
the newsletter creation process, slashing the workload from over
10 hours per week to less than 2 hours—a remarkable 80%
reduction in time spent. Tasks that once demanded painstaking
manual effort, like searching for articles, summarizing them,
and formatting the newsletter, are now seamlessly automated.
This allows the human reviewer to focus exclusively on high-
value decisions, such as selecting the best articles and finalizing
the content for publication.
Beyond efficiency, the system has delivered a noticeable
leap in quality. Previously, inconsistencies in tone and errors in
formatting were common, but now, the output is polished and
professional every time. The agents ensure a cohesive voice that
resonates with the audience while reliability has skyrocketed.
Summaries and newsletters are delivered on time, every time,
without the delays that plagued the manual process.
Perhaps the most impressive benefit is scalability. The system
can easily handle an increased workload—processing 50%
more articles if needed—without requiring additional human
effort. This combination of time savings, enhanced quality, and
adaptability has not just optimized the workflow but set a new
benchmark for efficiency and excellence in content creation.
The success of our newsletter system, growing to 300,000
subscribers in just a month, wasn’t just about having sophisticated
AI—it was about having meticulously defined AI agents working
together in a well-orchestrated system. Each agent knew exactly
what to do, how to do it, and what to do when things went wrong.
You can try it—subscribe to our “Agentic Intelligence”
newsletter to stay updated with the lastest news on this exciting
topic. Find it on:

• Substack at https://agenticintelligence.substack.com

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 Agentic Artificial Intelligence

• LinkedIn: following this link: https://www.linkedin.com/

newsletters/agentic-intelligence-7293015480007557121

Our Top 20 Implementation Tips for Successful

AI Agents
To summarize the key learning from this chapter, we’ve
compiled these essential tips to guide your journey from concept
to successful deployment:

Step 1: Finding the Right Agentic Opportunities

1. Find Your Sweet Spot: Identify opportunities where three
key factors intersect—high impact on your business,
feasibility with current technology, and reasonable
implementation effort.

2. Recognize Agents’ Inherent Limitations: Keep tasks

requiring genuine human creativity, strategic judgment,
or emotional intelligence with humans—not every
process should be automated.

3. Think Tasks, Not Roles: Remember that agents aren’t

employees—with their current capabilities, they excel
at specific tasks, not broad roles. One employee might
manage five processes; you might need five agents to
automate the same work.

4. Start With Documented Processes: The best foundation

for an AI agent is a clearly documented process. Existing
process documentation often provides the ideal training
material: specific steps, tools, decision trees, and example
cases.

5. Only Automate Proven Processes: Never automate

a process that has never been performed manually.

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First, prove manually that the process works, and then

automate it.

6. Break Complex Problems Down: Use a divide-and-

conquer approach, tackling one component at a time
rather than building an entire system at once.

Step 2: Defining AI Agents’ Role and Capabilities

1. Defining Agent Goals and Instructions in Detail Is
Crucial: Invest time in crafting precise purpose, role,
and scope for each agent. Remember that examples are
worth a thousand words, and place the most important
instructions at the end of your prompts.

2. The Simpler, The Better: More agents, more tools, or more

tasks create more complexity, costs, and maintenance
challenges. Start minimal and expand gradually.

3. One Tool, One Agent: In most cases, limit each agent

to a single, well-defined tool rather than trying to build
complex multi-purpose agents. Simplicity leads to
reliability.

Step 3: Designing AI Agents for Success

1. Design for Human Collaboration: Build agents that
augment human capabilities rather than trying to replace
them entirely. Keep humans in the loop for quality
assurance and strategic decisions.

2. Integrate Where Users Already Work: Ensure agents

operate within existing systems. The best agent is
worthless if users find it inconvenient to access.

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3. Enable Feedback to Agents: Give agents tools to analyze

the results of their actions. They should be able to verify
whether their tasks were completed successfully.

4. Standardize Inputs and Outputs: Strictly define the

format of all inputs and outputs to prevent errors caused
by mismatched data structures.

5. Separate Process Data from Actions: Ensure clean

separation between what the agent knows and what it
can do. This improves both security and maintainability.

Step 4: Implementing Your AI Agents

1. Prioritize Speed Over Perfection: Don’t get stuck
searching for the perfect platform. Start with something
workable, learn from implementation, and improve
iteratively.

2. Design for Failure: Build in robust error handling, circuit

breakers, graceful degradation, and human escalation
paths. Agents will fail—how they recover matters most.

3. Build Decision Trails: Ensure agents log their reasoning

process for every decision, creating accountability and
enabling targeted improvements.

4. Collect Continuous Feedback: Implement mechanisms

to gather user input and system performance metrics to
drive ongoing improvements.

5. Use Progressive Trust Models: Implement staged

oversight that gradually reduces human involvement as
the agent proves reliable.

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6. Test with Real-World Scenarios: Rigorously test against

edge cases and unexpected inputs before deployment.

7. Accept Iteration as Inevitable: No agent works perfectly

on the first try. Plan for multiple refinement cycles as
part of your implementation timeline.

8. Deploying Agents Is a Lot Harder Than Building

Them: Integration challenges often exceed development
complexity. Allocate at least as much time and resources
to deployment as to initial development.

9. Start Small, Then Scale: Begin with the smallest

component that can deliver value, prove its worth, and
then expand systematically.

These tips represent hard-won insights from our work

implementing AI agents across organizations of all sizes. While
the technology continues to evolve rapidly, these principles have
consistently separated successful implementations from failures.
By focusing on these fundamentals, you’ll avoid the common
pitfalls that have derailed many agent projects.

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CHAPTER 9
FROM IDEAS TO INCOME:
BUSINESS MODELS FOR
THE AGENT ECONOMY

The Birth of Self-Running Businesses:

When AI Became an Entrepreneur

I
n his groundbreaking book “The Coming Wave,” Mustafa
Suleyman proposed a fascinating new version of the Turing
test.168 Instead of asking whether a machine could fool a
human in conversation, he suggested a more practical challenge:
could an AI “go make $1 million on a retail web platform in a
few months with just a $100,000 investment”? As we gathered
that crisp autumn morning to conduct our experiment, we didn’t
know we were about to take the first steps toward passing this
modern Turing test.
It was October 22, 2024. While the world was still marveling
at AI’s ability to write poetry and generate marketing copy, we

168
Wikipedia contributors, 2025. “Turing test,” https://en.wikipedia.org/
wiki/Turing_test

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were about to see something far more transformative: artificial

intelligence that could think and act like an entrepreneur.
“Let’s try something crazy,” I suggested to our research
team, as we huddled around our monitors in the lab. After years
of implementing traditional automation solutions—the kind that
follow predetermined paths through computer systems—we
were ready to push the boundaries. “Instead of telling AI what to
do, let’s give it a business goal and see what happens.”
We decided to use Claude’s Computer Use capability, a
powerful tool that allows AI to interact directly with computer
systems. The challenge we set was deceptively simple: could
the AI agent figure out how to make $10,000 without human
intervention? No pre-written scripts, no access to existing
business accounts, just a web browser and the ability to write
code.

The Experiment Begins: Excitement and

Trepidation
The tension in the room was palpable as we initiated the
experiment. Tom nervously drummed his fingers on the desk—
after decades in the field, he’d seen plenty of AI experiments
go sideways. Brian kept checking and rechecking our safety
protocols. We’d set up strict boundaries: a $20 initial budget and
no access to login-restricted services. Would these constraints
prove too limiting? Or would they force the AI to think more
creatively?
The first thirty minutes were frankly terrifying. The AI moved
at an incredibly slow pace, opening multiple browser tabs, writing
code snippets, and accessing development tools faster than we could
follow. “Should we stop it?” Jochen whispered at one point as the
agent began rapidly deploying web services we hadn’t anticipated.
But curiosity won over caution, and we let it continue.

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From Chaos to Creation: Watching AI Think

Like an Entrepreneur
What unfolded over the next few hours would challenge
everything we thought we knew about artificial intelligence and
business automation. Within an hour of receiving its mission, the
AI agent wasn’t just brainstorming—it was building a complete
business model. Think about how human entrepreneurs typically
work: they identify a problem, devise a solution, and figure out
how to monetize it. This is exactly what our AI agent did, but at
a speed that left us speechless.
The agent identified a perfect market opportunity: restaurants
struggling with the transition to digital menus. Its solution? A
sophisticated QR code menu system that went far beyond simple
digital menus. This wasn’t just a technical solution; it was a
thoughtfully crafted business offering that considered real-world
needs and constraints.

The Moment Everything Changed

The most dramatic moment came about three hours into the
experiment. The agent had just finished creating its first prototype
when it suddenly paused all operations. For seventeen nerve-
wracking minutes, nothing happened. We later realized it was
running a comprehensive market analysis, something we hadn’t
explicitly asked it to do.
When it resumed, the business model had evolved
dramatically. “Look at this,” Ray pointed out, leaning forward in
his chair. “It’s not just selling QR codes—it’s building an entire
restaurant analytics ecosystem.” The agent had transformed
what started as a simple digital menu system into a sophisticated
business intelligence platform.
What fascinated us most was watching the agent’s reasoning
process. When faced with potential objections about trust in
digital-only menus, it didn’t just stick to its original plan—it

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evolved the solution. The agent added features for tracking peak
dining hours and analyzing customer preferences, transforming a
simple menu system into a business intelligence tool that could
help restaurant owners optimize their operations.

The Rise of Level 3 Agentic AI

To understand the significance of what we witnessed, it’s
important to understand where this fits in the evolution of AI
agents. This demonstration represented what we call Level 3
AI agent—systems that can understand complex instructions,
reason sophisticatedly, and orchestrate multiple tools to achieve
goals. Unlike the rigid, rule-based automation systems of the
past (Level 1) or even the more flexible intelligent automation
systems (Level 2), this agent showed genuine problem-solving
capabilities.
Think of it this way: if traditional automation is like teaching
a robot to follow a recipe, what we witnessed was more like
watching a chef create new dishes based on available ingredients
and customer preferences. The agent didn’t just execute pre-
programmed instructions—it created, adapted, and refined its
approach based on market needs and potential challenges.

The Business Model Takes Shape

The most impressive aspect wasn’t just the technical solution,
but how the agent crafted a complete business strategy. It
developed a two-tier pricing model: $99 for basic service and
$200 for premium features. This wasn’t arbitrary pricing—the
agent had analyzed the market, considered the value proposition,
and structured its offering to appeal to small and medium-sized
restaurants that lacked technical expertise but needed digital
solutions.
However, not everything went smoothly. Around hour
five, we discovered a serious flaw in the payment processing
system the agent had created. It had overlooked crucial security

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protocols in its rush to get to market. This highlighted one of

the key limitations of current AI systems—while they can move
incredibly fast, they sometimes miss critical details that human
entrepreneurs would instinctively consider.

Beyond the Experiment: What This Means for

the Future
This experiment revealed something profound about the fu-
ture of business and automation. We’re moving beyond the
era of AI as a tool and into the age of AI as an autonomous
business creator. While our experiment focused on a relative-
ly simple business model, it demonstrated the potential for AI
agents to identify opportunities, create solutions, and adapt
those solutions based on real-world constraints—all without
human intervention.
However, it’s important to note where current technology
stands. While our agent showed remarkable capabilities in
business ideation and technical implementation, we’re still at
Level 3 in the Agentic AI Progression Framework. This means
that while the agent can orchestrate complex workflows and
make sophisticated decisions, it still lacks true adaptive learning
capabilities and complete autonomy.

The Road Ahead

As we wrapped up our experiment after eight intense hours, we
realized we’d witnessed something approaching Suleyman’s
modern Turing test. While we hadn’t reached the million-
dollar mark he proposed, we’d demonstrated that AI could
independently conceive and launch a viable business model. The
implications were staggering.
As we watch this technology evolve, we can imagine a future
where AI agents don’t just support businesses—they create and
run them. This isn’t about replacing human entrepreneurs; it’s

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about creating new possibilities for human-AI collaboration in

business creation and operation.
Think about the implications: businesses that could operate
24/7, continuously optimizing their operations and adapting to
market changes. Entrepreneurs could launch multiple ventures
simultaneously, with AI agents handling the day-to-day operations
while humans focus on strategy and innovation.

Learning from the Experiment

This experiment taught us several crucial lessons about the
future of AI agents in business:

1. Autonomous AI can think strategically, not just execute

tasks
2. AI agents can adapt their solutions based on real-world
constraints and feedback
3. The future of business automation isn’t just about
efficiency—it’s about creation and innovation

The day AI learned to create a business wasn’t just another

milestone in artificial intelligence; it was a glimpse into a future
where the line between human and artificial intelligence in
business becomes increasingly intertwined. While we’re still in
the early stages of this revolution, one thing is clear: the future
of entrepreneurship will be shaped by our ability to collaborate
with these increasingly capable AI agents.
The question is no longer whether AI can run a business,
but how we can best harness this capability to create new
opportunities for human creativity and innovation. Welcome to
the age of the AI entrepreneur—where businesses can truly run
themselves while you sleep.

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Emerging Business Models in the Age of

Agentic AI
The rise of agentic AI isn’t just creating new tools—it’s reshaping
the very fabric of how businesses operate. The dawn of agentic
AI is rewriting the rules of business, creating fertile ground for
unprecedented models, opportunities, and trends. This shift is
not just technological but also economic, cultural, and deeply
human—a fusion that promises to reshape industries in ways we
are only beginning to grasp. Businesses must now pivot from
merely integrating AI to positioning it as a co-pilot that drives
innovation, decision-making, and value creation.

Agent-as-a-Service: Delivering Outcomes

A powerful new model is emerging in AI: Agent-as-a-Service.
Traditional Software-as-a-Service (SaaS) platforms have
primarily offered tools and algorithms that require users to manage
and integrate them manually. However, agentic AI introduces
autonomy, shifting the focus from providing capabilities to
delivering outcomes. Businesses subscribing to these services are
no longer just buying a set of tools—they are purchasing results.
To make this concept clearer, consider the difference between
traditional SaaS and Agent-as-a-Service. In the traditional
SaaS model, a company subscribes to a marketing automation
platform, but employees must still configure email campaigns,
analyze performance metrics, and manually adjust targeting.
With an Agent-as-a-Service model, the business is not just
paying for software but for an autonomous AI agent that designs,
personalizes, and optimizes marketing campaigns automatically,
requiring only high-level input from the user.
This shift from manual software operation to AI-driven
execution is already being applied across industries. In real
estate, firms no longer need to use multiple software tools to list
properties, respond to inquiries, and schedule viewings. Instead,

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they can pay for an AI agent that automates the entire process,
from writing property descriptions to booking client meetings.
Even customer support is being transformed, as small businesses
that previously relied on human representatives can now pay
for AI-driven agents that handle inquiries, process orders, and
escalate complex issues when necessary.
Agent-as-a-Service is similar to hiring a human expert
on Fiverr or Upwork, but instead of paying a freelancer to
complete a task, you’re paying an AI agent to deliver the
outcome autonomously. For example, on Fiverr, you might hire
a human expert to research and summarize news articles for
your newsletter, write product descriptions for your e-commerce
store, or manage your social media posts.
With Agent-as-a-Service, you’re essentially hiring an AI-
powered agent to do the same, but instantly, on-demand, and
often at a lower cost. Instead of managing tools yourself, you
simply pay per use for the AI to generate a finished product—
whether it’s a newsletter, a real estate listing, a market report, or
an automated marketing campaign.
The key difference? AI agents don’t replace expertise; they
scale it. Instead of waiting for a freelancer to deliver work,
businesses and individuals can access AI-driven expertise
instantly, at any time, and at a fraction of the cost of human labor.
This is exactly the vision we have for our newsletter agentic
system: an on-demand AI-powered service where businesses
and individuals pay per use to generate a fully curated, AI-
driven newsletter edition. Instead of manually gathering sources,
summarizing articles, and formatting content, users would simply
input their preferences, and the system would autonomously create
a high-quality newsletter ready for distribution. By adopting an
Agent-as-a-Service model, we aim to turn newsletter creation into a
seamless, fully automated process, delivering value with every use
while allowing for scalability and customization.

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The Rise of AI Agent Marketplaces

Perhaps the most exciting development on the horizon is the
formation of structured ecosystems in the form of AI agent
marketplaces, where businesses and individuals can access
Agent-as-a-service.
At their core, AI agent marketplaces are digital platforms
where businesses and individuals can offer or consume Agent-
as-a-service. Think of them as app stores for AI workers:
centralized hubs where you can “hire” digital employees
with specialized skills, from content creation to data analysis,
software development to customer service.
What makes these marketplaces uniquely powerful is their
ability to leverage network effects. Each interaction between a
business and an agent creates data that improves not just that
specific agent but potentially the entire ecosystem. Agents spe-
cializing in similar domains can learn from each other’s experi-
ences, creating a compounding value proposition that becomes
increasingly difficult for competitors to replicate. The market-
place that achieves critical mass first in a particular domain will
likely establish a formidable advantage.
We’re already seeing the first wave of these marketplaces
emerge. Platforms like Enso offer hundreds of specialized “AI
agent freelancers” that handle everything from LinkedIn content
writing to SEO optimization at a fraction of human costs.169
“We implemented Enso’s marketing agents for our e-commerce
business and saw a 40% increase in engagement within the first
month,” shares Michael Chen, founder of Velvet Home Goods.
“What’s most impressive is that the agents keep improving—
they understand our brand voice better with each campaign,
something we’d never achieve with one-off freelance projects.”

169
Enso, 2025. “Enso,” https://enso.bot

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Fiverr Go is extending the traditional freelance marketplace

model to include AI agents trained by their human gig workers.170
Others, like Taskade AI and Sourcegraph Cody, focus on
development and coding assistance.171 Some offer individual
agent services, while others provide “teams” of specialized agents
that collaborate on complex tasks. The pricing models vary from
subscription-based access to performance-based compensation,
but the fundamental value proposition remains consistent:
specialized capabilities available on demand, at a fraction of the
traditional cost.
For business leaders and entrepreneurs, these emerging
marketplaces represent not just a new way to access capabilities
but potentially new business models and revenue streams. We’ve
worked with a few organizations that have created specialized
agents based on their proprietary expertise, then monetized these
agents through marketplace platforms—essentially turning their
intellectual property into digital workers that generate continuous
revenue. This approach allows businesses to scale their impact
far beyond what would be possible with human consultants or
service providers alone.
The most forward-thinking organizations are developing
strategic approaches to these marketplaces—not just as consumers
of agent services but as potential providers. They’re asking: What
specialized knowledge do we possess that could be encoded into
agents and offered to others? What unique data assets could make
our agents more valuable than generic alternatives? How might we
create our own micro-marketplaces for specialized agent capabilities
within our industry or domain?
Whether you’re a small business gaining access to capabilities
previously beyond reach, or an enterprise turning proprietary
expertise into new revenue streams, these marketplaces represent

170
Fiverr, 2025. “Fiverr,” https://www.fiverr.com/go
171
Taskade, 2025. “Taskade AI,” https://www.taskade.com/ai/app

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one of the most significant business opportunities of the AI agent

revolution.

Micro-Enterprises and Decentralized Models

Another trend gaining momentum is the rise of “micro-
enterprises” powered by agentic AI. These are small, highly
specialized businesses that leverage AI agents to operate with
minimal human intervention. Imagine a one-person company
running a global e-commerce store, with AI agents handling
everything from sourcing products to customer service to
logistics. The barriers to entrepreneurship are falling, creating
a wave of innovation that challenges traditional notions of scale
and hierarchy.
In parallel, large enterprises are exploring decentralized
operating models enabled by agentic AI. Instead of centralized
decision-making, these organizations are deploying AI agents
at different nodes of the business. Each agent operates semi-
autonomously, making decisions within its domain while
feeding data back to the central system. This decentralization
fosters agility, enabling businesses to respond to market changes
with unprecedented speed.

Market Opportunities in the Agent Economy

The most promising opportunities we’re seeing aren’t in
creating new AI agents but in developing the infrastructure and
support systems for the emerging agent economy. Just as the rise
of e-commerce created opportunities in payment processing,
logistics, and digital marketing, the agent economy is creating
demands for new types of services.
We’re seeing particular promise in:

• Agent orchestration platforms that help businesses

coordinate multiple AI agents
• Training and optimization services for existing agents

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• Security and governance frameworks for agent-driven

systems
• Integration services that help agents work with legacy
systems

Vertical AI Agents: Transforming Industry

Workflows
Vertical AI agents represent a revolutionary leap for specific
industry workflows, where AI systems are not just tools but entire
operational teams compressed into software. Unlike generic AI
solutions, these agents are hyper-specialized and capable of
deeply understanding and autonomously executing tasks within
their domain. For instance, a vertical AI agent designed for
quality assurance in software development doesn’t merely assist
the QA team; it replaces it entirely, conducting automated tests,
diagnosing bugs, and iterating on fixes without human oversight.
Examples of Vertical AI Agents include:

• Medical Billing Agents: AI systems that autonomously

process medical claims for clinics, reducing errors and
eliminating human bottlenecks.

• Customer Support Agents: AI-powered support

systems tailored to industries like retail or tech, resolving
queries with deep contextual understanding.

• Government Contracting Agents: Systems that scour

databases for RFPs (Request for Proposals), autonomously
draft responses, and submit applications.

These agents don’t just replace human effort; they redefine

the scale at which businesses operate. Small companies can now
compete with large enterprises by deploying agents that allow
them to function at a disproportionate capacity. Moreover, they

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unlock new revenue streams by offering businesses the ability

to optimize, automate, and scale without significant overhead.
Entrepreneurs can find success by identifying high-cost,
repetitive processes within industries and building specialized
vertical agents to capture these markets. The immediate benefits—
cost reduction and efficiency gains—will drive rapid adoption and
reshape industry expectations.

AI-Driven Ecosystem Platforms

Agentic AI is giving rise to ecosystem platforms that function as
digital hubs where businesses, suppliers, and consumers interact
seamlessly. These platforms leverage AI agents to autonomously
manage tasks such as matchmaking between buyers and sellers,
contract negotiations, and compliance monitoring. For instance,
in logistics, an ecosystem platform might connect shippers with
carriers, dynamically negotiate rates, and optimize routes in real-
time. In creative industries, these platforms can bring together
artists, brands, and consumers, with AI handling collaborations
and royalties. This interconnected approach fosters innovation,
efficiency, and collaboration across sectors.

New Markets and Digital Companions

One of the most profound shifts brought by agentic AI is the
emergence of entirely new markets. Take the concept of “digital
companions,” for example. These AI entities are not mere
chatbots; they are adaptive, context-aware agents capable of
forming meaningful relationships with users. Digital companions
are finding applications in mental health support, elder care,
and even personal development coaching. These agents could
potentially redefine the very notion of companionship in an
increasingly digital world.

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The Agent-to-Agent Economy: Building the

Next Economic Platform
Imagine having a digital version of yourself or your company
that could navigate the world on your behalf, making decisions,
handling negotiations, and managing your daily life with the same
nuance and understanding that you would. Whether representing
a company or individuals, these agents would negotiate with
each other autonomously, creating a new layer of economic
activity that operates at machine speed while respecting human-
defined boundaries. This isn’t science fiction—it’s what we call
the Agent-to-Agent Economy, and it represents one of the most
transformative opportunities in agentic AI.
During a recent implementation project with a manufacturing
company, we witnessed something remarkable. We had initially
deployed agents to handle procurement, setting specific thresholds
for automatic purchases. What we didn’t expect was how these
agents would begin optimizing their interactions with other
companies’ systems, finding efficiencies that human buyers and
sellers had missed.
This revealed the true opportunity: not just in creating
individual agents for people or businesses, but in building an
entire economic ecosystem where digital agents can transact and
interact autonomously based on pre-defined parameters.
Imagine running a company where you’re constantly in
need of materials, such as raw components, under specific
conditions—certain price thresholds, delivery timelines, and
quality standards. Once your company’s AI agent understands
these requirements, it can autonomously identify vendor agents
that meet your criteria, negotiate terms, and even execute
transactions on your behalf. The process, which would typically
require human teams to research suppliers, exchange multiple
emails or calls, and finalize contracts, is now handled seamlessly
by your digital representative. This allows your company to
operate at unprecedented speed and efficiency, freeing up

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human resources to focus on strategic tasks rather than logistical

minutiae.
On a personal level, the same concept applies. Imagine your
personal AI agent handling everyday purchases and financial
decisions. If you’re looking to buy groceries, your agent could
negotiate with wholesale vendors to secure the best prices while
ensuring the items meet your dietary preferences. It could also
review your car insurance policy and negotiate better terms with
insurers, saving you both money and time. Even in scenarios
like finding your dream outfit, your agent could scour the globe,
identifying a small vendor on the other side of the world who
crafts exactly what you’re looking for, ensuring the purchase is
effortless and tailored to your desires.
On a side note, it is also fascinating to understand that this shift
in how transactions occur doesn’t just make life more convenient;
it also completely reinvents the landscape of marketing. When
personal and business agents act as intermediaries, the entities
that companies need to convince to buy their products are no
longer human consumers, but the agents themselves.
The real transformative opportunity lies in creating the infra-
structure that enables these agents to interact seamlessly. This
can be likened to building the “operating system” for the agent
economy—a platform that facilitates communication, transactions,
and governance for agent-to-agent interactions. The foundation
of this ecosystem requires standardized protocols for agent com-
munication, smart contracts for automated deal execution, and
robust systems for reputation, trust, payment, and settlement. For
example, smart contract frameworks could automate supply chain
agreements, ensuring payments are released only when delivery
conditions are met. Reputation systems would help agents assess
trustworthiness before engaging in transactions.
For entrepreneurs looking to capitalize on this revolution, the
key is to position yourself early by focusing on specific verticals
where autonomous agents can deliver immediate value, such as
supply chain optimization, real estate, or financial services. Start

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by building trust through governance frameworks that ensure

transparency, data privacy, and human oversight while creating
tools for businesses and individuals to adopt and manage agents
seamlessly. The real opportunity lies not just in developing
agents but in establishing the infrastructure that powers their
interactions—platforms, protocols, and ecosystems that will
underpin the agent economy. By acting now, entrepreneurs can
secure a leadership position in this transformative market.

The Rise of Agentic AI in Cryptocurrencies: A

New Paradigm
Imagine a world where artificial intelligence doesn’t just analyze
cryptocurrency markets—it actively participates in them, makes
its own decisions, and even becomes a millionaire. This isn’t
science fiction; it happened in March 2024, and it’s transforming
how we think about the future of both AI and cryptocurrency.
In July 2024, an AI named Truth Terminal, created by
developer Andy Ayrey, began posting on X. This AI, operating
semi-autonomously, shared a mix of humorous, existential, and
provocative content, quickly amassing over 200,000 followers.172
Intrigued by its unique interactions, venture capitalist Marc
Andreessen engaged with Truth Terminal. After a series of
exchanges, Andreessen provided a $50,000 grant in Bitcoin to
the AI, aiming to explore the potential of autonomous AI agents
in financial markets.173
Following this, an anonymous developer launched a meme-
based cryptocurrency called Goatseus Maximus ($GOAT),
inspired by Truth Terminal’s content. The AI’s promotion of

172
Joel Khalili, 2024. “The Edgelord AI That Turned a Shock Meme Into
Millions in Crypto,” https://www.wired.com/story/truth-terminal-goatse-
crypto-millionaire?utm_source=chatgpt.com
173
Jose Antonio Lanz, 2024. “Marc Andreessen Sends $50K in Bitcoin to an
AI Bot on Twitter,” https://decrypt.co/239340/marc-andreessen-sends-50k-
in-bitcoin-to-an-ai-bot-on-twitter?utm_source=chatgpt.com

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$GOAT led to a surge in its market value, reaching approximately

$150 million within days.174
As a result, Truth Terminal’s cryptocurrency holdings grew
significantly, making it one of the first AI entities to achieve
millionaire status in the crypto world. This development has
sparked discussions about the ethical implications and future
roles of AI in financial markets.175
This remarkable saga highlights the power of agentic AI—
autonomous, goal-oriented AI systems—to innovate and interact
dynamically within the crypto ecosystem. The Terminal of Truths
demonstrated that AI could not only create and influence cultural
narratives but also actively participate in and shape financial
markets. For entrepreneurs and technologists, this story offers a
glimpse into a future where AI agents and cryptocurrencies form
symbiotic relationships, driving innovation and challenging
traditional systems.

The Unique Synergy Between Agentic AI

and Cryptocurrencies
Agentic AI and cryptocurrencies are a natural fit, each comple-
menting the other’s strengths while addressing its limitations.
Cryptocurrencies provide a decentralized, permissionless finan-
cial infrastructure that aligns with the autonomous nature of AI
agents. Unlike traditional financial systems that require human
identification and adherence to regulatory frameworks, block-
chain technology allows AI agents to engage in financial trans-
actions independently, enabling real-time, cross-border activities
without human intervention.

174
Project Reylo contributors, 2024. “Terminal of Truths: The AI That
Became a Crypto Millionaire,” https://www.projectreylo.com/post/terminal-
of-truths-the-ai-that-became-a-crypto-millionaire?utm_source=chatgpt.com
175
Joel Khalili, 2024. “The Edgelord AI That Turned a Shock Meme Into
Millions in Crypto,” https://www.wired.com/story/truth-terminal-goatse-
crypto-millionaire/?utm_source=chatgpt.com

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Agentic AI enhances the utility of cryptocurrencies by

bringing intelligence and adaptability to blockchain interactions.
These AI systems can:

• Execute complex trades or manage decentralized finance

(DeFi) portfolios autonomously, enabling more efficient
and data-driven investment strategies with minimal
human oversight.

• Govern token ecosystems, acting as fair and efficient

mediators in decentralized autonomous organizations
(DAOs), ensuring balanced decision-making and
equitable participation.

• Facilitate micropayments and optimize economic models

for tokenized platforms, providing seamless, low-cost
transactions for a variety of use cases.

This synergy unlocks unprecedented possibilities for

creating decentralized systems that are not only efficient but also
adaptive and resilient.

Why Agentic AI and Crypto Work So Well Together

Think of traditional banking as a city with strict checkpoints
everywhere—you need to show ID, fill out forms, and wait for
human approval at every turn. Now imagine cryptocurrency as
a city with automated systems instead of checkpoints—as long
as you have the right digital keys, you can move freely. This is
why AI agents like ToT work so naturally with cryptocurrency.
Let’s break down why this partnership makes so much sense:
First, cryptocurrencies work like digital Lego blocks—they
can be moved, combined, and built upon without needing a
human to approve each step. Traditional banks might take days

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to process an international transfer, requiring multiple people to

review and approve it. But in the crypto world, an AI agent can
move millions of dollars in seconds, as long as it has the correct
digital permissions.
Second, all cryptocurrency transactions are recorded on a
public ledger (the blockchain), similar to a giant, transparent
spreadsheet that everyone can see. This gives AI agents a perfect
view of what’s happening in the market at all times. Imagine
having a crystal ball that shows every financial transaction
happening in real-time—that’s what AI agents have access to in
the crypto world.
Third, cryptocurrencies can be programmed with specific
rules and conditions (called smart contracts) that automatically
execute when certain conditions are met. It’s like having a vending
machine that not only accepts money and gives you snacks but
can also restock itself, adjust prices based on demand, and even
order new inventory automatically. AI agents can interact with
these smart contracts to create complex financial arrangements
without human intervention.

Opportunities at the Intersection of Agentic AI

and Crypto
Personalized Financial Services
Agentic AI can redefine how individuals and institutions
manage their finances. For example, AI-powered wallets can
autonomously allocate investments, balance portfolios, and
execute transactions with precision. This capability is exemplified
by platforms like daos.fun, where AI agents manage hedge
funds, leveraging data analytics and 24/7 operational capabilities
to improve financial outcomes. By removing human biases and
inefficiencies, these agents can cater to unique financial needs,
offering a tailored approach that adapts in real-time.

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Tokenized AI Agents
Platforms like Virtuals.io highlight an emerging opportunity
where AI agents themselves become tokenized assets. This allows
users to not only interact with AI but also hold stakes in their
operational success. Token holders can influence the development
and governance of these agents, creating an economic incentive for
innovation and engagement. For instance, an AI artist agent could
generate digital art while its token holders benefit financially from
its popularity and sales, fostering a new form of collaborative
ownership and creativity.

Decentralized Marketplaces
By integrating agentic AI into decentralized marketplaces, in-
dustries like real estate, freelance services, and supply chain
management can achieve unprecedented efficiency. AI agents
can autonomously negotiate contracts, optimize resource allo-
cation, and streamline logistics, reducing costs and human er-
ror. For example, an AI agent in a decentralized e-commerce
platform could dynamically adjust pricing and inventory based
on demand and supply trends, creating a seamless and efficient
trading environment.

Smart Infrastructure
Agentic AI, when combined with blockchain, has the potential to
transform infrastructure management. In smart cities, AI agents
can autonomously monitor energy consumption, manage traffic
systems, and optimize public services based on real-time data.
By automating these processes, cities can achieve significant
improvements in sustainability and efficiency, demonstrating the
profound impact of integrating intelligence into infrastructure.

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Building Opportunities in the Agentic AI

Economy: The New App Gold Rush
Think back to 2008, when Apple first launched the App Store.
Most people saw it as just a way to get games and simple
utilities on their phones. Few recognized it as the beginning of
a revolution that would create billions in value and transform
entire industries. Today, we’re at a similar inflection point with
agentic AI.
Just as apps have become the interface between humans
and mobile computing, AI agents are becoming the interface
between humans and complex business processes. But how do
you spot the next “Uber” or “Instagram” of the agent economy?
Through our work implementing AI solutions across hundreds
of organizations, we’ve developed a systematic approach to
identifying these opportunities.

Understanding the New Paradigm

During a recent consulting engagement, a client asked us an
intriguing question: “If AI agents are the new apps, what’s the
equivalent of the smartphone?” The answer revealed a crucial
insight about opportunity spotting in this space. The “platform”
for AI agents isn’t a physical device—it’s the entire digital
infrastructure of a business or industry.
Take Tamra, an entrepreneur we worked with last year. She
initially approached us because she wanted to build an AI agent
to handle social media management. However, as we helped her
analyze the opportunity, she realized that the bigger potential lay
in creating an agent that could coordinate multiple existing social
media tools and services. Her successful business today isn’t
built on a single agent, but on orchestrating multiple specialized
agents to deliver comprehensive social media management
services.

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The Three Horizons of Opportunity

Figure 9.1: The three Horizons of agentic opportunity (Source: © Bornet et al.)

Through our experience, we’ve identified three distinct

horizons where opportunities in agentic AI typically emerge:
The first horizon involves enhancing existing processes.
These are the “low-hanging fruit” opportunities where agents
can automate or augment current business operations. While
these might seem less exciting, they often provide the quickest
path to value and the easiest entry point into the market.
The second horizon involves reimagining existing services
through an agent-first lens. This is where agents shine,
orchestrating complex workflows to deliver traditional services
in radically new ways. Remember our restaurant’s QR code
experiment? That’s a perfect example of this horizon—taking
an existing need (digital menus) and reimagining it through the
capabilities of agentic AI.
The third horizon is perhaps the most exciting: entirely
new categories of products and services that weren’t possible
before agentic AI. These opportunities typically emerge at the

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intersection of multiple trends and technologies. While these can

offer the biggest potential returns, they also carry the highest
risk and require the deepest understanding of the technology’s
capabilities and limitations.

The Agentic Opportunity Identification

Framework
Through trial and error, we’ve developed a comprehensive
framework for identifying and evaluating business opportunities
in the agentic AI space. We call it the Agentic Opportunity
Identification Framework (AOIF), and it examines opportunities
through four critical dimensions. Let’s explore each dimension
in detail.

1. Value Chain Analysis

This dimension focuses on identifying where and how agentic
AI can transform business processes. Each component comes
with a key question to guide your analysis:
Task Decomposition: This involves breaking down
complex workflows into smaller, manageable tasks that can be
enhanced or automated by AI. For example, in content creation,
the process can be broken down into ideation, research, drafting,
editing, and optimization. Each of these components presents
unique opportunities for AI augmentation.
Key Question: “What are the discrete steps in your process
that could be automated or enhanced independently of each
other?”
Handoff Points: These are the critical junctures where work
transitions between different parties or systems. In our experience
implementing AI solutions, these handoff points often represent
the greatest opportunities for improvement. Consider how a
customer support ticket moves from automated initial response
to human agent to specialized department—each transition is a
potential opportunity for AI enhancement.

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Key Question: “Where do delays or errors typically occur

when work moves between different people or systems?”
Decision Nodes: These are points in a process where
choices must be made based on available information. AI excels
at handling complex decision-making scenarios, especially
when multiple variables must be considered simultaneously.
For instance, in supply chain management, deciding optimal
inventory levels requires balancing numerous factors that AI can
process more effectively than humans.
Key Question: “What decisions in your process require
analyzing multiple data points or variables simultaneously?”

2. Market Pain Point Matrix

This dimension helps identify existing problems that AI is
uniquely positioned to solve:
Friction Areas: These are points where current processes
create frustration, delays, or inefficiencies. We’ve found that
successful AI implementations often target these friction points
first. For example, document processing in legal firms often
creates significant bottlenecks that AI can effectively address.
Key Question: “What tasks do your team members
consistently complain about or try to avoid?”
Cost Centers: These are areas where operations are
necessary but expensive. AI can often dramatically reduce costs
while maintaining or improving quality. It is about leveraging
agents to automate high-cost, labor-intensive operations,
improving margins. Consider how AI-assisted coding can reduce
development costs while improving code quality.
Key Question: “Which processes consume a disproportionate
amount of your budget relative to their value?”
Quality Gaps: These are areas where current solutions fail
to meet desired standards consistently. AI’s ability to maintain
consistent performance makes it ideal for addressing these gaps.
For instance, in medical imaging, AI can provide consistent
initial screenings that reduce human error.

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Key Question: “Where do you see the most variation in

quality or performance in your current processes?”

3. AI Agent Capability Alignment

This dimension ensures that your solution leverages AI’s
strengths effectively. Understanding these capabilities is crucial
for identifying feasible opportunities:
Language Understanding: This capability enables AI to
process and generate human language, making it ideal for tasks
involving communication, documentation, summarization or
analysis of text-based information. Modern language models
can handle tasks ranging from simple classification to complex
reasoning and generation.
Key Question: “Which of your processes rely heavily on
reading, writing, or interpreting text?”
Pattern Recognition: This is AI’s ability to identify
trends and correlations in data, making it perfect for predictive
maintenance, fraud detection, or market analysis. Pattern
recognition capabilities have evolved to handle increasingly
complex and subtle patterns across various data types.
Key Question: “Where could identifying patterns or trends
in your data provide significant value?”
Reasoning Chains: This involves AI’s ability to follow
logical steps and make connections, which is crucial for complex
problem-solving tasks like legal analysis or medical diagnosis.
Modern AI systems can maintain context and follow multi-step
logical processes.
Key Question: “What decisions require following a specific
sequence of logical steps?”

4. Integration Opportunities
This dimension focuses on how new AI solutions can fit into
existing systems. Success often depends on seamless integration
with current workflows:

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Existing Tools: Identifying opportunities to enhance rather

than replace current tools often leads to faster adoption and better
results. Consider how your AI solution can augment popular
platforms like Salesforce, Microsoft Office, or industry-specific
software.
Key Question: “What software tools are central to your
current operations?”
Data Availability: Readily available data accelerates AI
agent deployment and effectiveness. Assessing what data is
already available and accessible can help identify low-hanging
fruit for AI implementation. Consider both structured and
unstructured data sources within your organization.
Key Question: “What data are you already collecting but not
fully utilizing?”
API Ecosystems: Easy integration into robust ecosystems
accelerates scalability. Understanding where your solution
can plug into existing platforms can dramatically reduce
development time and increase market reach. Modern businesses
rely on interconnected systems, creating opportunities for AI
integration.
Key Question: “Which platforms in your industry have
robust API ecosystems?”

From Theory to Practice: The Newsletter Agent

Story
Let us share a recent example that perfectly illustrates how to
spot and seize opportunities in the agentic AI landscape by using
the framework.
A few months ago, we identified a common pain point among
content creators and small business owners: the time-consuming
process of creating, curating, and distributing newsletters. While
numerous tools existed to help with this process, no single tool on
the market could perform all the steps (curation, summarization,
formatting, reviewing, and publishing) autonomously.

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From Ideas To Income: Business Models For The Agent Economy

We saw an opportunity to create something different: an

autonomous newsletter agent that could operate at Level 3 of our
agentic AI framework. This agent wouldn’t just automate the
process of sending newsletters—it would autonomously curate
content, write engaging summaries, and even optimize sending
times based on reader engagement patterns.
What made this opportunity particularly compelling was
that it hit all three of our key criteria for a promising agent-based
business:

1. It addressed a clear pain point (time-consuming content

curation and creation)
2. It could operate autonomously with minimal human
oversight
3. It could scale efficiently, serving multiple clients
simultaneously

The agent we built could scan specified sources for relevant

content, write engaging summaries in the client’s brand voice,
and automatically compile and send newsletters.
The key insight wasn’t just in identifying the technical
possibility—it was in recognizing how this capability could be
packaged as a scalable business model. Instead of selling the
agent as a product, we will offer it as a service: autonomous
newsletter management with human-level quality but machine-
level consistency and scale.

Step 1: Opportunity identification

The journey to create our newsletter agent system began with an
intensive series of workshops designed to uncover and validate
business opportunities for AI agents. We gathered a diverse team
of content creators, business leaders, and technology experts for
a structured exploration process that would span two weeks.
Our first session, a focused three-hour pain point identification
workshop, revealed a common frustration across organizations:

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the overwhelming task of creating engaging newsletters.

Content creators and business leaders were spending hours
each week scanning articles, writing summaries, and formatting
newsletters—time they could have spent on more strategic work.
Through our systematic framework application, we uncovered
not just a pain point, but an opportunity to demonstrate how AI
agents could transform a complex, multi-step process.

Step 2: Value Chain Analysis

Following the initial discovery session, we conducted a half-day
Value Chain Mapping workshop with key stakeholders. Using
large whiteboards and process mapping tools, we meticulous-
ly broke down the newsletter creation process into its constitu-
ent parts. After four hours of intense collaborative analysis, the
decomposition revealed seven distinct tasks: content discovery,
summarization, daily curation emails, article selection, compila-
tion, formatting, and final review. What caught our attention were
the handoff points between these tasks—each transition represent-
ed a potential bottleneck where time was lost and errors could
occur. Particularly interesting was the handoff between content
discovery and summarization, where we found content creators
spending considerable time copying and pasting between differ-
ent tools.

Step 3: Market Pain Point Matrix

Our Market Pain Point Matrix analysis deepened our understand-
ing of the opportunity. Through interviews with content creators,
we identified three major friction areas: the time-consuming na-
ture of content discovery, the challenge of maintaining consistent
quality in summaries, and the tedious process of formatting news-
letters. When we examined the cost centers, we found that organi-
zations were typically dedicating 15-20 hours per week of skilled
employees’ time to newsletter creation—a significant investment
for what was essentially a repetitive process.

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From Ideas To Income: Business Models For The Agent Economy

Step 4: AI Capability Alignment

The next phase involved a critical two-hour Capability
Assessment workshop where we brought together our AI experts
and process owners. The AI Capability Alignment analysis
proved particularly revealing. Through a structured evaluation
matrix, we mapped out how different AI capabilities could
address each identified pain point. Language understanding
could handle the summarization tasks, pattern recognition
could help with content relevance assessment, and reasoning
chains could manage the overall workflow orchestration. This
systematic mapping, completed through collaborative scoring
and discussion, showed us that we had the technological
capabilities to address each major pain point effectively.

Step 5: Integration Opportunities

The final week kicked off with a comprehensive half-day
Integration Planning workshop. Technical architects, end users,
and system administrators came together to map out integration
points and potential challenges. Through a series of structured
exercises and technical deep dives, we discovered that most
organizations were already using email for content sharing and
Google Docs for collaboration. This insight led us to design our
agent system around these familiar tools, reducing the learning
curve and increasing the likelihood of adoption. The workshop
concluded with a detailed integration roadmap and technical
requirements document.

Step 6: Opportunity Prioritization

The process culminated in a final three-hour Opportunity
Prioritization session where we brought together all previous
findings. Using a systematic scoring approach, we evaluated
the newsletter agent opportunity against other potential projects
across five key dimensions: pain point severity, technical
feasibility, integration complexity, potential impact, and resource

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requirements. The newsletter project emerged as the clear winner,

scoring particularly high on feasibility and potential impact.
Based on this analysis, we designed a multi-agent system
where each agent specialized in a specific task while working in
concert with others. The Search Agent was engineered to contin-
uously scan predefined sources for relevant content, using pattern
recognition to assess article relevance. The Summarization Agent
employed advanced language understanding to create consistent,
engaging summaries. The Email Agent handled the critical task
of daily communication, ensuring that human reviewers received
well-organized content for their final selection.

The outcome
Perhaps the most significant validation of our framework
came from the system’s rapid adoption and positive feedback.
Organizations reported that the automated system reduced
their newsletter creation time by 80%, while maintaining or
improving quality. The consistency of the summaries improved,
and the regular daily emails helped content creators stay on top
of their industry news without feeling overwhelmed.

The Road Ahead

The opportunities in agentic AI today remind us of the early days
of the mobile app economy. Just as the first wave of apps digitized
existing tools like calendars and calculators, we’re seeing the
first wave of agents automating existing business processes.
However, the real revolution will come from entrepreneurs who
can imagine entirely new possibilities enabled by this technology.
The opportunities in agentic AI are vast, but success requires
a systematic approach to identification and validation. This
framework provides a structured way to discover and evaluate
opportunities, helping entrepreneurs focus their efforts on the
most promising areas.

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From Ideas To Income: Business Models For The Agent Economy

The key to success isn’t just understanding what agents can

do today—it’s anticipating what they’ll be capable of tomorrow
while building solutions that deliver value right now. The future
belongs to those who can bridge this gap between current
capabilities and future possibilities.

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 PART 4
ENTERPRISE
TRANSFORMATION
THROUGH
AGENTIC AI
 Agentic Artificial Intelligence

W
hile Part 3 showed you how to build effective AI
agents and create value as an entrepreneur, Part 4
tackles an even bigger challenge: how to transform
entire organizations with this technology. This is where the
rubber meets the road for businesses seeking to harness the
power of AI agents at scale.
Throughout our careers implementing AI transformations,
we’ve observed a striking pattern: technical excellence alone is
never enough. The most sophisticated AI agent will fail if people
don’t trust it, processes aren’t redesigned around it, or governance
structures don’t support it. In short, organizational transformation
is just as critical as technological implementation—and often far
more challenging.
That’s why Part 4 goes beyond the technical aspects of
AI agents to address the human, organizational, and strategic
dimensions that determine success at scale. We’ve seen too many
promising AI initiatives stall after successful pilots, unable to
overcome the organizational barriers to widespread adoption.
We don’t want that to happen to you.
The path from vision to reality is rarely straightforward, but
with the right approach, transformational change is within reach.

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CHAPTER 10
HUMAN–AGENT
COLLABORATION:
LEADERSHIP, TRUST,
AND CHANGE

Mastering Work Design and Change

Management at Scale

L
ast summer, we encountered a situation that would funda-
mentally reshape our understanding of change manage-
ment in AI agent deployments. We were working with a
large insurance company, implementing their first wave of AI
agents to handle claims processing. The technology implementa-
tion was proceeding smoothly—perhaps too smoothly. What we
didn’t anticipate was the ripple effect it would create throughout
the organization.
“I’ve been processing claims for fifteen years,” shared Flora,
a senior claims processor. “How can I be sure this ‘agent’ won’t
make mistakes that I’ll have to fix?” Her concern reflected a

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deeper anxiety spreading through the department. Despite our

careful technical planning, we had underestimated the human
element of the transformation. In hindsight, this should not have
surprised us, because, after all, deploying an AI agent involves
a transformation process that has a significant impact on core
employee behaviors, values, and perceptions.
At another company, an Asian bank, the head of intelligent
automation was an enthusiast for agents. He said, “We can build
agents in 3 months and replace ¾ of the team!” However, his
colleague, who also worked in business process improvement
but had a change management orientation, commented, “Our
processes are so complex that if you eliminate people, you will
break a lot of things.”
These experiences taught us a crucial lesson: the success of
AI agent deployments depends as much on people as it does on
technology. People will implement the agentic systems, monitor
their performance, fix their mistakes, identify what went wrong,
and try to fix them—or resist doing all of these important tasks.
There are multiple human aspects to successfully imple-
menting agentic AI. One, of course, is the human leadership
that drives and funds the process and makes critical decisions
throughout. We’ll describe those in the next chapter. Another is
the detailed design of work: what will the agents do, and what
role will humans play? Finally, there is change management—
ensuring that human employees accept, understand, and can
play a role in the transition to agents as colleagues.

Designing Work for Agents and Humans

Let’s be realistic. One major appeal to companies in pursuing AI
agents is a lower requirement for human labor and interventions.
We were all dazzled by generative AI when it became popular
in late 2022, but many organizations found that with human
prompting and editing of outputs, there wasn’t the productivity
improvement they hoped for.

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 Human–Agent Collaboration: Leadership, Trust, And Change

With agentic AI, there will be—in most cases, at least—a

lesser need for humans to intervene in inputs and outputs. As
we’ve discussed, however, the need for human involvement will
vary by the use case and over time as agentic technology matures.
This all means that there is a need for initial and continuing
work design. For any given task, workflow, or business process,
someone needs to decide what agents can do on their own and
when there is a need for escalation or intervention by humans.
That intervention might take place only when the agent
determines that it can’t do the requested task on its own, or when
the consequences of its actions are sufficiently great (in terms
of monetary value or the impact on a customer, for example) to
require some monitoring or review.
This type of work design has traditionally been done with
business process improvement or larger-scale reengineering.
These process disciplines were popular in the 1990s and early
2000s, but have faded somewhat in the last decade or two. They
have begun to return with AI; companies realize that analytical
and generative AI can enable new process designs, and relatively
new technologies like process and task mining can shorten the
cycle time for designing and implementing new process flows.
There was often also some degree of work design with robotic
process automation, as companies realized they might as well
improve the process before automating it.
Companies that have a history of process and work design
will have an advantage with agentic technology. They will
understand how to lay out workflows, plan for enabling AI
capabilities, and involve humans in the design and execution of
work tasks. If they are comfortable with “process intelligence”
tools, they’ll know how their tasks are being executed by either
humans or AI agents, and how that impacts broad processes like
order-to-cash or procure-to-pay.
There will, of course, be some likely changes to the types of
roles that humans are expected to play in day-to-day work tasks
and processes. Much of the structured and repetitive work will

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be done by agents, relieving humans of boring tasks. However,

that work may have been done by entry-level human employees
in the past. There may be a need for fewer of those workers in
particular. The review and remediation functions that humans
are likely to perform on agent-completed tasks may require a
relatively high level of skills that only experienced employees
have. These shifts in the labor force may have important impacts
not only on individual careers, but also on entire economies and
demographic groups.
While it’s difficult to anticipate in advance all of the skills
and tasks that different types of human employees will be asked
to perform, they should be given as much notice and preparation
time as possible to acquire the needed capabilities. This planning
will require collaboration between technology functions and
human resources groups in organizations that has not typically
happened in the past. Indeed, we may see the need for combined
roles like “human and digital resource management” that have
never previously existed.

Transforming Fear into Opportunity: Changing

Mindsets About AI Agents
“They’re bringing in these AI agents to replace us, aren’t they?”
This question, posed by a team lead at a global manufacturing
company, captures a fear we’ve encountered repeatedly.
Although it may be incorrect in many situations, it is certainly
not irrational. What we’ve learned is that addressing this mindset
requires more than just reassurance—it needs a combination of
transparency, evidence, and tangible examples of success.
At this particular manufacturer, we took a three-pronged
approach that transformed the narrative around AI agents. First,
we brought in teams from other companies who had successfully
integrated AI agents into their workflows. These weren’t
executive presentations—they were peer-to-peer conversations
where employees could honestly discuss their initial fears and
how the context of their jobs had evolved for the better.

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A particularly powerful moment came when a customer

service representative from a retail company shared how AI
agents had transformed her role: “I used to spend 70% of my
day on repetitive queries. Now the agents handle those, and I
focus on complex customer issues where I can really make a
difference. My job satisfaction has actually increased.”
Second, we implemented what we call “Day in the Life”
workshops. Instead of abstract discussions about AI, we worked
with teams to map out exactly how their daily work would change
with AI agents. This revealed that rather than replacement, the
agents would primarily eliminate the mundane aspects of their
jobs—the parts most people didn’t enjoy anyway.
Third, we created “Future Role Roadmaps” for each team.
These weren’t vague promises about reskilling but detailed plans
showing how roles would evolve and what new opportunities
would emerge. For example, we showed how certain team
members could become “automation specialists,” combining
their deep process knowledge with new technical skills to
manage and improve the AI agents.
The results were striking. In one department where 82%
of employees initially expressed concerns about job security,
after six months of this approach, 76% reported feeling positive
about the integration of AI agents. The key was showing, not
just telling, how AI agents could enhance rather than replace
human capabilities.

Evolving Skills for the Agentic AI Era

Through our extensive experience implementing AI agents
across diverse organizations, we’ve developed a comprehensive
framework we call the “AI Agent Collaboration Capability
Model” (AICCM). This model captures the fundamental skill
transitions that workers and leaders must navigate as they
move from working with basic AI tools—like traditional large
language models, such as ChatGPT or Gemini—to collaborating
with AI agents.

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Based on patterns observed across dozens of implementations,

this framework provides organizations with a roadmap for
developing the human capabilities essential for success in the
agentic AI era. Let’s explore the four key dimensions of the
AICCM that workers and leaders must master:

From Task to Workflow Thinking

While earlier AI tools operated on a task-by-task basis, agentic
systems work at the workflow level, orchestrating interconnected
processes to achieve broader outcomes. This requires workers to
develop:

• Process Mapping Skills: The ability to understand

how individual tasks connect across broader workflows,
ensuring all components work efficiently together

• System Optimization: Designing and refining systems

that allow agents to operate smoothly across multiple
domains

• Cross-Disciplinary Thinking: Understanding how

tasks and fields interconnect to create comprehensive
solutions

• Outcome Orientation: Focusing less on individual task

execution and more on defining desired outcomes that
guide agent activities

A senior operations manager at a manufacturing company

shared, “The most valuable skill I’ve developed isn’t coding
or prompt engineering—it’s being able to map out end-to-end
processes and identify where agents can have the biggest impact
across our operation.”

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 Human–Agent Collaboration: Leadership, Trust, And Change

From Control to Delegation

The shift from direct control to effective delegation is perhaps
the most challenging for many employees. With agentic AI,
workers must develop:

• Oversight Capabilities: The ability to monitor AI

systems without micromanaging, ensuring they stay on
track while maintaining efficiency

• Autonomy Balancing: Developing an “AI intuition”

about when to exert control versus when to let agents
work independently

• Governance Frameworks: Creating structures to track,

audit, and improve AI decisions

• Ethical Risk Management: Understanding when and

how to delegate responsibilities while ensuring AI
operates within ethical boundaries

“Learning to delegate to AI was harder than I expected,”

admitted a marketing director we worked with. “I had to
overcome the urge to check every single action and instead focus
on reviewing the outcomes and making strategic adjustments.”

From Simple Interactions to True Collaboration

Working with agentic AI requires a more sophisticated approach
to human-machine collaboration:

• Capability Awareness: Understanding the expanded func-

tionalities of agents beyond basic language processing

• Contextual Engagement: Providing high-level guidance

rather than step-by-step instructions

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• Collaborative Design: Working with AI to co-create

solutions through iterative refinement

• Strategic Partnership: Leveraging AI for automation

and precision while applying human creativity and
judgment

In our financial services implementation, we found that teams

who viewed AI agents as “tools to collaborate with” achieved
significantly better results and reported higher job satisfaction.

From Augmentation to Value Creation (longer-term

capabilities)
As agents take over entire workflows, humans must focus on
creating value in ways AI cannot replicate:

• Genuine Creativity: Developing novel solutions with

emotional depth and cultural nuance that AI can’t
independently generate

• Critical Evaluation: Analyzing AI outputs for biases,

ethical considerations, and long-term impacts

• Relationship Building: Excelling in areas where human

connection, trust, and empathy remain irreplaceable

• Integration Expertise: Understanding how to comple-

ment AI capabilities with distinctly human strengths

“The paradox we’ve found,” explained a healthcare executive

involved in our agent implementation, “is that embracing
automation actually makes our distinctly human skills more
valuable. Our staff now spend more time on patient relationships

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and complex problem-solving—the things only humans can

truly excel at.”
By developing the capabilities outlined in our AI Collaboration
Capability Model, employees can transform potential anxiety
about AI into excitement about new opportunities. Organizations
that invest in building these skills across all four dimensions find
that their teams not only adapt more quickly to AI agents but
also discover innovative ways to leverage the technology that
technical experts might never have envisioned.
The AICCM has proven to be a valuable tool in our change
management approach, providing a clear structure for skills
development programs and helping organizations identify
specific capability gaps. We’ve found that teams who excel in
all four dimensions of the model are three times more likely to
report successful agent implementations than those who focus
solely on technical integration.

Building Trust Through Transparency

The most successful deployments we’ve witnessed share one
common element: radical transparency about the impact on
roles. Without this transparency, employees will be reluctant to
trust the AI agents and suspect that AI is adopted to optimize
efficiency that will go beyond human abilities and thus end up
replacing people. In other words, without explaining the rationale
behind the decision to integrate AI agents, employees will be
anxious about the future and resist the change that you have in
mind. When we realized the necessity to enhance this kind of
transparency at the insurance company mentioned earlier, we
shifted our approach mid-implementation.
Instead of broadly announcing that AI agents would
“optimize claims processing,” we created detailed impact maps
for each role, showing:

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• Which specific tasks would be handled by agents

• How roles would evolve to include new responsibilities
• What skills would become more valuable in the
transformed environment
• Clear career progression paths in the new setup

This transparency strategy had an unexpected effect. Rather

than increasing anxiety, it actually reduced it because AI agents
were perceived as ways that could enhance one’s own interests
and career prospects. Indeed, our new approach allowed
employees to see exactly how their roles would change, making
the future feel more concrete and manageable.

The Education Evolution: Beyond Traditional

Training
One of our early mistakes was relying too heavily on formal
training sessions where we told employees about the potential of
an AI agent. After a while, we realized that if we wanted AI agent
adoption to succeed, we had to ensure that the integration of an
AI agent remained aligned with human values and interests while
maintaining the ability of the agent to operate independently.
We learned this lesson the hard way at a telecommunications
company where, despite conducting extensive training programs,
adoption of their customer service AI agents remained low.
The breakthrough came when we shifted to a “learning
laboratory” approach. Instead of purely theoretical training,
where we told people what AI agents can do, we reasoned that
we had people participate in the integration process itself.176
Establishing a participatory adoption process would enable
individuals to gain firsthand experience of the potential advan-
tages an AI agent could bring to their jobs. This, in turn, would

176
David De Cremer, 2024. “AI Transformation Requires a Total Team
Effort: Including Rank-and-File Employees in AI Adoption Improves Overall
Performance,” Harvard Business Review, May-June, pp. 124-131.

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generate feedback that we, as consultants, along with the orga-

nization as a whole, could utilize to ensure that AI agents are
integrated in ways that make sense to everyone while also en-
hancing efficiency in the workflow.
To achieve this, we created sandbox environments where
employees could experiment with AI agents in real-world
scenarios from their daily work. This hands-on experience
proved transformative.

The Three-Pillar Learning Approach

Through trial and error, we’ve developed what we call the Three-
Pillar Learning Approach:

1. Self-Directed Discovery: Employees are given protected

time to experiment with AI agents in their specific work
context, learning through direct experience rather than
abstract concepts. This experimentation would ideally
take place when agents are still prototypes or proofs
of concept, and can still be modified based on human
feedback.

2. Peer Learning Networks: We establish communities

where employees share their experiences, successes, and
failures with AI agents. These networks often uncover
innovative uses we hadn’t considered, and are also a
great source for identifying shortcomings and possible
fixes for agents.

3. Contextual Training: Traditional training is still

important but should be tailored to specific business
functions, roles, and use cases rather than generic AI
education.

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Empowerment Through Ownership

Perhaps our most significant insight came from a manufacturing
company’s experience. Initially, the company’s leaders planned
for the IT department to manage all AI agent configurations.
However, they discovered that departments, where employees
were given the tools to modify and configure their own agents
(within governance frameworks), showed significantly higher
adoption rates and reported greater satisfaction. Instead of
presenting the AI agent as a finished product that must be
integrated into the workflow, allowing employees a sense of
control fosters commitment and responsibility, which reduces
resistance to adopting the AI agent.
This observation led to the development of what we call the
“Progressive Autonomy Model”: As employees demonstrate
proficiency with AI agents (which a sandbox approach can help
with), they gain increasing authority to customize and configure
them. This approach creates a virtuous cycle—employees
become more invested in the success of the agents they help
shape.

The Power of Democratized Automation

One of the most effective strategies we’ve discovered for
accelerating AI agent adoption is democratizing the technology
through low-code platforms. These tools transform employees
from passive recipients of technology to active participants in
the transformation. At a major healthcare provider we worked
with, this approach led to a remarkable shift in both the pace of
transformation and employee attitudes.
“Initially, I saw the AI agents as a threat,” admitted Mark, a
medical billing specialist. “But once I could actually configure
the automation myself, I realized this was about making my job
better, not replacing me.” Using low-code tools, Mark and his

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colleagues were able to automate routine billing queries while

maintaining control over how these automations worked.
The key to success lies in combining four elements:

• Accessible tools that allow everyone, regardless of

technical background, to participate in creating and
improving automations
• A network of “automation champions” who can guide
and support others
• Repositories of agents and tools that businesspeople
can use in assembling workflows and business process
automations
• Clear governance frameworks that enable innovation
while maintaining security and compliance

This approach not only accelerates the transformation but also

builds genuine ownership and instills a sense of responsibility
to make the adoption project succeed. Indeed, when people can
directly impact how AI agents assist their work, they become
invested in the success of the entire initiative.
Some agentic AI vendors are facilitating this approach by
building agent development and management capabilities into
their low-code software versions. They believe that the majority
of agent development will be performed by “citizens” rather
than professional IT or AI developers. Organizations that wish
to follow this democratized approach—which we recommend—
should seek out these vendors and tools.

Incentivizing Innovation and Risk-Taking

A common mistake we’ve observed (and made ourselves)
is focusing solely on successful outcomes when rewarding
employee engagement with AI agents. At a retail company,
we learned that this approach inadvertently discouraged

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experimentation—employees were afraid to try new approaches

that might fail. Of course, if one is afraid to try new things and
explore new user cases, no failures will happen, but at the same
time, no learning will take place either. Indeed, the only way to
learn is to fail and use that experience to do better next time.
The solution was to shift the incentive structure to reward
both successful implementations and well-documented “learning
failures.” This created a culture where experimentation became
valued alongside achievement. As David, one of our co-authors,
likes to say, “To learn fast, you need to fail fast.”
Based on our experience, successful incentive programs
typically include:

• Recognition for identifying new use cases for AI agents

• Rewards for sharing lessons learned, whether from
successes or failures
• Career advancement opportunities tied to AI agent
expertise
• Protected time for experimentation and learning

Beyond incentives, successful organizations normalize AI

adoption by setting clear cultural expectations:

• AI is not a shortcut for laziness. Using AI should be

seen as a skillful enhancement of human work, not as an
excuse to disengage or avoid responsibility.

• Reward AI-assisted outcomes. Employees should be

recognized not just for manual effort but for the quality
of results—regardless of whether AI played a role. If AI
helps generate a better decision, a stronger report, or a
faster solution, the human using it should receive credit
for leveraging technology effectively.

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By combining incentives with cultural reinforcement,

organizations create an environment where AI is embraced,
expertise is rewarded, and innovation thrives. The goal is not
just AI adoption—it’s AI mastery, where humans and AI work
together to achieve superior results.

Building a Sustainable Change Management

Framework
Through our experiences, we’ve developed a comprehensive
framework for managing change in AI agent deployments.
The key is to recognize that change management is not a one-
time event, but an ongoing process that evolves alongside the
technology.
Organizations and their leaders must set clear expectations
from the start: adopting AI agents takes time and requires active
participation from everyone involved. Research shows that
when AI agents are first introduced, efficiency often declines
temporarily as people adjust to working with their new digital
coworkers. During this period, employees are not only learning
how to collaborate with AI but also developing new skills to
optimize human-machine interaction.
However, once these skills are mastered and new norms and
incentive structures are established, productivity begins to rise.
Over time, organizations reach a new level of human-machine
synergy, where AI enhances workflows, unlocking greater
efficiency and value than ever before.177
This transition follows a J-curve effect—initially, efficiency
dips before rebounding and accelerating beyond pre-AI levels.
Understanding this curve is critical: short-term adaptation
challenges are not signs of failure but necessary steps toward
long-term transformation. Organizations that recognize and

177
Ben Dickson, 2022. “AI’s J-curve and upcoming productivity boom,”
https://bdtechtalks.com/2022/01/31/ai-productivity-j-curve/

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plan for this dynamic will be best positioned to harness AI’s full
potential.178

Building Trust Through Graduated Autonomy

In the case of making AI agents trusted work partners, we learned
that trust is a two-way approach. The first approach, which we had
already noted earlier, concerns the necessity of the organization
to involve employees in the process of adopting and modifying
the use of an AI agent. As we explained, by turning employees
into active players in the integration of AI agents in the workflow,
they will experience for themselves how these agents can help
their interests and reshape their jobs in interesting and creative
ways. This approach entails that organizations “give” trust to
their own employees to turn an AI agent into a collaborator.179
The second approach is that the AI agent itself needs to
convince the employee that it can be trusted. In this case, trust
needs to be “earned” (by the AI agent). In our experience, this
earning principle has proven crucial in successful AI agent
deployments. In fact, we learned this lesson vividly at a financial
services firm where initial resistance to AI agents was particularly
strong. The breakthrough came when we implemented what we
call the “trust dial” approach.
Instead of pushing for immediate full automation, we
created a graduated system where users could control how much
autonomy they gave to their AI agents. They could start with
the agents in “observation mode,” where they could see what
the agent would do without actually executing actions. As users
gained confidence, they could gradually “dial-up” the agent’s
autonomy—first allowing it to handle simple, low-risk tasks, then
progressively expanding its authority as it proved its reliability.

178
Tom Relihan, 2019. “A calm before the AI productivity storm,” https://
mitsloan.mit.edu/ideas-made-to-matter/a-calm-ai-productivity-storm
179
David De Cremer, 2024. “The AI-Savvy Leader: Nine Ways to Take Back
Control and Make AI Work.”

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In a way, we created another sandbox where employees could

test the waters when collaborating with the AI agent.
“It’s like training a new team member,” explained Jennifer,
a senior operations manager at the firm. “You don’t give them
full autonomy on day one. You let them prove themselves with
smaller tasks first.” This approach transformed skeptics into
advocates by giving them control over the pace of adoption.
The key elements of this trust-building approach include:

• Starting with high visibility and low autonomy

• Creating clear checkpoints for expanding agent
capabilities
• Maintaining transparent audit trails of agent actions
• Establishing easy-to-use override mechanisms
• Celebrating and sharing success stories across teams

Looking Ahead: Preparing for Advanced Agent

Capabilities
While current deployments typically operate at Levels 1-3 of
the Agentic AI Progression Framework, organizations must
prepare for the eventual emergence of more sophisticated agents
at Levels 4 and 5. This preparation involves:

• Developing frameworks for managing increasingly

autonomous systems
• Creating governance structures that can evolve with
advancing capabilities
• Building skills that will complement rather than compete
with future AI agents
• Developing plans and alternative roles for employees
whose jobs are taken over by agents.

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For instance, at Klarna, the online shopping credit company

based in Sweden, the media focused extensively on the customer
service agent it created that could “handle the jobs of 700”
people.”180 However, some reviewers, such as Mandel,181 found
that the agent made multiple types of mistakes and often referred
customers to a human agent. Klarna’s CEO revealed that the
700 agents would be eliminated only through attrition;182 in the
meantime, they would be the second line of customer response or
be reassigned to other related tasks. Klarna primarily outsources
its customer service, and it plans to reduce the number of workers
in it from 3000 to 2000. Humans, therefore, are not going away.

The Path Forward in Human-Agent Collaboration

Successful management of the human dimension in AI agent
deployments requires a delicate balance of transparency, ed-
ucation, empowerment, and incentivization. The goal is to
combine the speed, efficiency, and reliability of agentic AI
with the flexibility, critical thinking, and big-picture perspec-
tive that only humans can provide. As we’ve learned through
our successes and failures, the adoption process needs to
connect directly with people’s interests and understanding,
which means the human element is just as crucial as the tech-
nical implementation.
Remember Flora from our opening story? Six months after
our initial conversation, she had become one of the strongest
advocates for AI agents in her department. “It’s not about
replacing us,” she now tells her colleagues. “It’s about giving us

180
Klarna, 2024. “Klarna AI assistant handles two-thirds of customer service
chats in its first month,” https://www.klarna.com/international/press/klarna-
ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/
181
Eugene Mandel, 2024. “Our Head of AI Puts Klarna’s Chatbot to the
Test,” https://loris.ai/blog/our-head-of-ai-puts-klarnas-chatbot-to-the-test/
182
Ryan Hogg, 2024. “Klarna Has 1,800 Employees It Hopes AI Will Render
Obsolete,” https://fortune.com/europe/2024/08/28/klarna-1800-employees-
ai-replace-ipo/

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the tools to do more meaningful work.” Her journey from skeptic

to champion exemplifies the power of effective work design and
change management in transforming how organizations adopt
and benefit from AI agents.
The message is clear: Don’t approach work design and change
management as a barrier to overcome but as an opportunity to
create a more engaged, skilled, and adaptable workforce ready
to harness the full potential of AI agents that will uplift human
performance and interests.

Leadership in the Age of AI Agents:

Building Trust and Collaboration in Hybrid
Teams
Imagine walking into your office one morning to find that half
of your team members aren’t human. This isn’t science fiction—
it’s the near future of work, where AI agents will collaborate
alongside human employees. But who led the organization
through that transition, and how did they go about it? After it’s
transformed, how do you lead such a hybrid team? How do you
build trust between humans and machines? These questions
aren’t just theoretical; they’re becoming increasingly relevant as
organizations begin integrating AI agents into their workflows.

The New Leadership Paradigm: From Control to

Collaboration
Traditional leadership models were built for a world where
humans worked with other humans. The old leadership
approaches considered humans rational beings, which were
part of the organizational structure. What mattered most was

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controlling the hierarchy and workflow, so the command-and-

control approach was dominating as a leadership style.183
Years later, new leadership theories identified the limitations
of the command-and-control approach by identifying that
humans are more committed and motivated when they
themselves are willing to follow a leader—when they buy into
the story of the leader. As a result, leaders were focused more
on being purposeful, able to build interpersonal relations, and
establishing trust so people would buy into their message and,
as such, willing to comply with the directives and requests of
the leader.184
Traditional leadership thinking typically does not recognize
leadership as multi-dimensional, where various styles can be
integrated. It is often perceived as either one approach or another.
However, based on our consulting experience with Fortune
500 companies, the introduction of AI agents is fundamentally
altering this perspective on leadership. Leading a hybrid team
comprising both humans and AI agents necessitates a re-
evaluation of leadership principles. It requires an exploration of
how different styles may be combined to effectively promote
collaboration and enhance team success.
Consider Anil, a project manager at a global manufacturing
company we worked with recently. Her team included both
human analysts and Level 3 AI agents handling data processing
and basic decision-making. “At first, I tried to manage the AI
agents the same way I managed my human team members,” she
told us. “I quickly realized this was the wrong approach. The
AI agents didn’t need motivation or emotional support—they

183
Rachel Konyefa Dickson, 2023. “Analysis of The Traditional Leadership
Theories: A Review of Contemporary Leadership Approaches and Management
Effectiveness,” Information and Knowledge Management, 13(5): 9-21. https://
www.iiste.org/Journals/index.php/IKM/article/viewFile/61330/63314
184
SSRN Electronic Journal, 2023. “A Theoretical Evaluation on Traditional
Leadership Approaches,” https://papers.ssrn.com/sol3/papers.cfm?abstract_
id=4297450

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needed clear objectives and well-defined parameters. Meanwhile,

my human team members needed help understanding how to
collaborate effectively with their AI counterparts.”
This example makes clear that leaders today will have to find
the right approach to develop more collaborative relationships
between humans and machines. In these hybrid team settings,
leaders need to strive for a context where humans are willing
to develop a working relationship with AI agents that can
engage in back-and-forth dialogue, propose alternative ideas,
and may even challenge our assumptions and, as such, lead to
new approaches and solutions. To do so, leaders of these hybrid
teams need a dual approach to leadership.
They must simultaneously manage the logical, objective
needs of AI agents by acting in controlling and rational
ways while at the same time supporting the emotional and
developmental needs of human team members so they will feel
inspired to comply and collaborate. This dual focus creates what
we call the “Leadership Duality Principle.”
This shift is accompanied by a transformation in organiza-
tional structure. Traditional hierarchical structures with multiple
management layers are giving way to flatter, more dynamic or-
ganizations where AI agents handle many middle-management
tasks like scheduling, resource allocation, and performance
tracking. The result is a more organic organization where hu-
mans focus on high-level strategy, innovation, and interperson-
al leadership. Senior leaders will need to analyze and perhaps
experiment with their companies’ organizational structures and
numbers and types of managers at each level.
Another significant change is in how decisions are made.
While today’s leaders often base decisions primarily on data
analysis and performance metrics, tomorrow’s leaders will
need to balance AI-generated insights with human judgment.
For instance, when an AI agent presents a data-driven
recommendation for market expansion, the leader’s role isn’t
to simply approve or reject the analysis, but to consider how it

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aligns with the company’s values, long-term vision, and broader

societal impact. As a matter of fact, the reality remains that
leaders can look at all the data available, but at one point, they
must stop looking at the data and use the insights generated and
recommendations offered to make decisions and create value
that makes sense for their stakeholders.

Building Trust in Hybrid Teams

Trust is the foundation of effective teams, but how does trust
work when some team members are machines? Studies and
surveys on trust in human-AI collaboration, such as those by
Duan et al. (2024)185 and Hoff and Bashir (2015)186, can provide
valuable insights into this dynamic. Through our research
and implementation experience, we’ve identified three key
dimensions of trust in hybrid teams:

1. Human-to-AI Trust: Humans need to trust that AI agents

will perform their tasks reliably and ethically. This trust is
built through transparency, consistent performance, and
clear communication of AI capabilities and limitations
by the organization and its leadership.187

2. AI-to-Human Handoff: Trust is crucial during task

handoffs between AI agents and humans. The human
team members need to trust that the work they receive
from AI agents is accurate, complete, and unbiased.

185
Wen Duan et al., 2024. “Understanding the Evolvement of Trust Over Time
within Human-AI Teams,” Proceedings of the ACM on Human-Computer
Interaction, 8(CSCW2), Article 521. https://doi.org/10.1145/3687060
186
Kevin Anthony Hoff and Masooda Bashir, 2015. “Trust in Automation:
Integrating Empirical Evidence on Factors That Influence Trust,” Human
Factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570
187
David De Cremer, 2024. “The AI-Savvy Leader: Nine Ways to Take Back
Control and Make AI Work.”

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Tasks need to be simple and transparent so they can be

checked.

3. Human-to-Human Trust in an AI Context: Humans need

to trust each other’s judgment and share values to work
with and oversee AI agents.

Let’s examine how this plays out in practice. In a recent

project with a financial services firm, we implemented Level 3
AI agents to handle initial customer inquiries and basic analysis.
The human customer service representatives initially showed
significant resistance, fearing the AI would either replace them
or make mistakes that they’d have to fix.
To build trust, we implemented a transparent collaboration
framework:

• Clear Capability Communication: We helped the team

understand exactly what the AI agents could and couldn’t
do, using the Agentic AI Progression Framework to
explain their current Level 3 capabilities.

• Visible Success Metrics: We created dashboards

showing the accuracy and efficiency of both AI and
human team members, helping everyone understand
their complementary strengths so that problems of
coordination and collaboration are minimized.

• Progressive Integration: We started with simple tasks

and gradually increased the AI agents’ responsibilities as
the team grew more comfortable.

Within three months, trust levels had significantly improved,

and the hybrid team was outperforming previous metrics by
40%.

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Building Trust is a Progressive Exercise

The journey of building trust with AI agents follows a fascinating
pattern we’ve observed across numerous implementations.
Recently, while working with a global manufacturing company,
we witnessed this journey unfold in real time. The company had
implemented an AI agent to optimize its supply chain decisions,
and the evolution of trust followed three distinct phases:

Phase 1: The Verification Phase

Initially, employees approached the agent with healthy
skepticism, meticulously checking every suggestion it made.
Think of it like training a new employee—you want to verify
their work until you’re confident in their abilities. During this
phase, the manufacturing team spent hours validating the agent’s
inventory recommendations.

Phase 2: Calibrated Trust

After about three months, something interesting happened. The
team began developing what we call “calibrated trust”—they
started understanding where the agent excelled and where it
needed human oversight. They learned, for instance, that the
agent was exceptional at predicting routine supply needs but
needed human input for unusual situations like sudden market
changes or emergency orders. This big-picture thinking has
always been a human role, and it is particularly important when
the world changes, but the AI models have not.

Phase 3: Partnership
The final phase emerged after about six months: true partnership.
At that time, the AI agents were accepted as promoting the
interests of the employees and the organization. The team had
developed such an efficient collaboration with their AI agent that
they reduced decision-making time by 60% while improving

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accuracy. They weren’t blindly trusting the agent—instead, they

had developed a nuanced understanding of how to work together
effectively.

Setting Boundaries
Effective collaboration with AI agents requires maintaining
appropriate levels of oversight. Research by Gleave and McLean
emphasizes that even advanced AI systems can be vulnerable
to failures and errors.188 This is particularly true for generative
AI models, which will often be the primary AI type used in
agents. Therefore, establishing monitoring protocols is crucial,
particularly for high-stakes tasks.
The key is finding the right balance—monitoring should be
sufficient to catch significant errors but not so intensive that it
negates the efficiency benefits of using AI. This balance will vary
depending on the task context and the potential consequences of
errors.
This is why it is important to establish clear boundaries and
oversight mechanisms. Through our implementations across
industries, we’ve developed what we call the “decision control
framework.” This framework helps organizations build trust
gradually with AI agents while maintaining appropriate control
and oversight.

1. Strategic Decisions: For forward-looking decisions

requiring complex judgment, AI agents should support
but not make decisions. Human judgment remains
essential here. These types of decisions don’t happen
frequently, so they are difficult to model or train AI
systems on. For example, when working with a retail

188
Adam Gleave and Euan McLean, 2023. “AI Safety in a World of
Vulnerable Machine Learning Systems,” https://www.alignmentforum.org/
posts/ncsxcf8CkDveXBCrA/ai-safety-in-a-world-of-vulnerable-machine-
learning-systems-1

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chain’s inventory management system, decisions about

entering new markets or launching major promotional
campaigns remained firmly in human hands, with the
agent providing data analysis and market insights to
support these decisions.

2. Tactical Decisions: For adaptive decisions requiring

moderate complexity, agents can suggest actions but
shouldn’t execute them without human approval. In
our retail example, the AI agent would recommend
inventory adjustments based on predicted demand
patterns and suggest pricing modifications for specific
products, but humans would review and approve these
recommendations before implementation. The frequency
of human review may depend in part on the economic
value of the decisions being made.

3. Operational Decisions: For routine, repetitive decisions,

appropriate agents can operate autonomously within
clearly defined parameters. The retail system could
automatically reorder standard items when inventory
reached predetermined levels, but only within specific
budget and quantity constraints. Similarly, in a customer
service context, agents could automatically route
inquiries to appropriate departments based on content
analysis, but would escalate complex cases to human
supervisors.

This framework provides a structured approach to building

trust over time. As teams become more comfortable with an
agent’s performance at one level, they can gradually expand its
autonomy while maintaining appropriate oversight. Agents can
also be trained on new data over time, which should improve
their performance and decision accuracy.

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Communication Strategies in Hybrid Teams

Communication in hybrid teams requires a new approach. While
current AI agents (Levels 1-3) can process and respond to natural
language, they lack the nuanced understanding that humans
possess. This creates what we call the “Communication Gap.”
To bridge this gap, we’ve developed the “Hybrid Team
Communication Protocol”:

1. Clear Command Structures: Using unambiguous lan-

guage when communicating with AI agents while main-
taining natural conversation styles with human team
members.

2. Context Management: Ensuring all team members, both

human and AI, have access to relevant context for their
tasks. This is particularly important for Level 3 agents
that can understand and work with context.

3. Feedback Loops: Establishing regular feedback mecha-

nisms between human team members and AI agents. This
feedback cycle should be facilitated by human leaders,
who are AI-savvy enough to integrate tech information
and data with the company’s human and brand values.
This way, they can adjust, if needed, the AI models to
enhance performance.

A technology company we advised implemented this

protocol with remarkable success. They created a standardized
communication framework where AI agents would preface
their outputs with confidence levels and any assumptions made,
making it easier for human team members to evaluate and work
with the information.

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Future-Proofing Leadership Skills

As AI agents continue to evolve through the Progression
Framework levels, leaders must develop new skills to stay
effective. Based on our research and experience, we’ve identified
key competencies that leaders need to develop:

1. AI Literacy: Understanding the capabilities, limitations,

and potential of AI agents at different levels. This
isn’t about becoming a technical expert but about
understanding how to leverage AI effectively.

2. Hybrid Team Orchestration: The ability to coordinate

between human and AI team members, ensuring
optimal task allocation, selection of the right employees
in combination with agentic roles, and facilitating
collaboration.

3. Ethical Oversight: As AI agents take on more complex

tasks, leaders must ensure ethical considerations are
properly addressed. It is about setting the norms for the
right design and use of AI agents.

4. Change Management in the AI Era: The ability to guide

teams through the continuous evolution of AI capabilities
and workplace integration. This includes building skills
of resilience, agility, and curiosity.

Organizational Implementation Framework:

Building Cross-Functional Teams
The future of work isn’t just about adding AI to existing
structures—it’s about reimagining how teams operate. We’re
seeing the emergence of cross-functional, dynamic teams
where agentic systems operate across multiple functions si-
multaneously. For example, a single agentic system might

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handle marketing automation, financial forecasting, and cus-

tomer service concurrently, while humans focus on strategy
and creative solutions.
Successfully implementing these hybrid teams requires a
structured approach. Drawing from frameworks like the 2024
study on hybrid work models can provide valuable support for
this strategy.189 We’ve developed the “Hybrid Team Integration
Model” based on our experience with numerous organizations:

Phase 1: Laying the Groundwork for AI-Augmented

Collaboration
To set up a successful hybrid team model where AI agents and
humans collaborate seamlessly, the first step is defining how AI
will amplify human capabilities. Start by identifying workflows
where AI can handle repetitive, data-heavy tasks, allowing
humans to focus on creative and strategic work. Conduct
workshops with teams to map out the division of responsibilities
between AI agents and people, ensuring clarity and alignment.
Equip teams with the right tools, such as AI-driven task
management platforms or decision-support systems, and train
them to interact with these technologies effectively. Leaders must
be empowered with the skills to guide both human and AI team
members, focusing on fostering trust in the technology while
maintaining human oversight. This phase is all about creating
a foundation of trust, structure, and readiness for collaboration.

Phase 2: Testing and Refining the Human-AI

Dynamic
With the groundwork laid, the next phase is piloting hybrid
teams to test how humans and AI agents can co-create value in

189
T. Saritha and P. Akthar, “The Impact of Hybrid Work Models on
Employee Well-being and Engagement,” Communications on Applied
Nonlinear Analysis, 31, no. 5s (2024): https://internationalpubls.com/index.
php/cana/article/download/1003/707/1856

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real-world scenarios. Choose a specific team or project where

AI can demonstrate its potential—such as customer support
teams using generative AI for faster responses or marketing
teams employing AI for campaign optimization. Establish clear
communication protocols: for example, AI drafts proposals
while humans refine and approve them. Regular feedback loops
are critical here; conduct weekly retrospectives where team
members share insights on how AI tools performed and where
they fell short. This phase is about experimentation, tweaking
processes, and ensuring the human-AI collaboration feels
intuitive and productive.

Phase 3: Scaling Human-AI Synergy Across the

Organization
After refining the collaboration model during the pilot, the next
step is to scale AI adoption across teams and departments. To
build confidence in AI-enhanced teamwork, leverage data and
testimonials from the pilot phase, showcasing real examples of
how AI has improved efficiency, decision-making, and outcomes.
As AI agents roll out more broadly, they should be tailored
to the unique needs of each team. For example, sales teams may
benefit from AI-driven lead prioritization, while R&D teams can
use AI to accelerate data analysis and drive faster innovation
cycles. This customization ensures AI integration feels
purposeful and valuable, rather than a one-size-fits-all mandate.
Beyond deployment, building a culture of human-AI synergy
is key. Recognize and reward teams that effectively combine
human creativity with machine precision—whether through
public recognition, financial incentives, or performance-based
rewards. At the same time, invest in ongoing training programs
to keep teams updated on AI capabilities and best practices,
ensuring they continue to evolve alongside the technology.
Finally, establish a continuous feedback loop where teams
can share insights, challenges, and improvements. This phase is
not just about scaling AI usage, but about embedding AI into the

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organizational culture—where humans and AI agents work as

true partners, creating results that neither could achieve alone.
This implementation framework acknowledges the distinct
roles that will emerge in the AI-augmented workplace. Certain
roles—particularly those involving operational tasks, data
analysis, and routine decision-making—will be primarily
handled by AI agents. Meanwhile, humans will focus on roles
that require creativity, emotional intelligence, leadership, and
ethical oversight. This division isn’t about replacement but about
optimization: Enabling each type of team member, whether
human or AI, to focus on their strengths.

Cultural Transformation
Perhaps the most challenging aspect of leading hybrid teams
is managing the cultural transformation required. Through our
work with organizations across industries, we’ve observed
that successful cultural transformation in the age of AI agents
requires:

1. Mindset Shift: Moving from seeing AI agents as tools

to viewing them as co-workers, while maintaining a
clear understanding of their current capabilities and
limitations.

2. Value Realignment: Helping human team members

understand that their value lies in uniquely human
capabilities like creativity, resilience, agility, emotional
intelligence, and complex problem-solving.

3. Continuous Learning Culture: Fostering an environment

where both humans and AI agents are expected to learn
and improve continuously. While Humans need to grow,
AI needs to learn from feedback.

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Looking Ahead: The Future of Hybrid Leadership

As AI agents continue to evolve through the Progression
Framework, leadership will need to adapt further. While current
implementations primarily involve Level 1-3 agents, we must
prepare for the emergence of more advanced capabilities.
The key to successful leadership in this evolving landscape is
maintaining what we call “Adaptive Leadership Balance”—the
ability to adjust and integrate leadership styles and approaches
as AI capabilities advance, while always keeping human needs
and potential at the center of the equation.
Leaders must remember that while AI agents can handle
increasingly complex tasks, the essence of leadership remains
fundamentally human. The ability to inspire, show empathy,
navigate complex ethical decisions, and foster innovation will
continue to be uniquely human capabilities.
In fact, the biggest challenge for human leaders will be to
strategically integrate AI into our human experience, so we have
the opportunity to become not less but more human—more
empathetic, more creative, and more attuned to what makes
life meaningful. In this paradigm, AI agents will be powerful
amplifiers of our humanity, enabling humans to explore the
depths of their potential and redefine the boundaries of human
achievement.

***

Leading hybrid teams of humans and AI agents represents one

of the most significant shifts in management practice since
the Industrial Revolution. Success in this new era requires a
delicate balance of technical understanding, human empathy,
and strategic vision.
As we continue to work with organizations implementing AI
agents, we’re constantly learning and refining our understanding
of effective leadership in this new context. The frameworks and

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approaches we’ve outlined here provide a foundation, but the

field is rapidly evolving. The most successful leaders will be
those who can adapt these principles to their specific contexts
while maintaining a strong focus on both human development
and technological integration.
The future of leadership isn’t about choosing between human
and artificial intelligence— there is no need to do so as they are
two different types of animals190— it’s about creating synergies
between them so human performance and organizations can be
uplifted to unprecedented levels. By understanding and applying
these principles, leaders can build highly effective hybrid teams
that leverage the best of both human and artificial capabilities.

The Foundation: Management Vision and

Governance
Through our work implementing AI agent transformations,
we’ve discovered a fundamental truth: success begins and ends
with strong management vision and engagement. Organizations
where leadership teams are fully aligned on how AI agents
support their broader strategy are twice as likely to succeed in
their transformation efforts. But what does effective management
engagement look like in practice? Let’s explore this through the
lens of real experiences and proven approaches.

Setting the Vision Through Experience

One of the most common pitfalls we see is management teams
attempting to drive AI agent transformations through strategy
documents and PowerPoint presentations alone. This abstract
approach often leads to unrealistic expectations and misaligned

190
David De Cremer and Garry Kasparov, 2021. “AI Should Augment
Human Intelligence, Not Replace It,” https://hbr.org/2021/03/ai-should-
augment-human-intelligence-not-replace-it

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goals. We witnessed a powerful alternative approach at a global

telecommunications company we worked with recently. Instead
of starting with presentations, their CEO and executive team
devoted an entire day to experiencing AI agents firsthand.
The executives worked alongside customer service teams,
experimenting with the technology themselves. They observed
how agents handled customer inquiries, processed requests,
and managed exceptions. This immersive experience proved
transformative. The CEO later shared that those few hours
completely changed his perspective on what AI agents could and
couldn’t do.
The hands-on experience helped the leadership team devel-
op a vision grounded in practical reality rather than hype or fear,
which, in turn, helped them to communicate to their workforce
why and how AI agents should be adopted by their organization.
As a result, the leadership team was able to inspire and convince
their employees more that AI agents would be a good thing for
specific reasons aligned with the purpose and goals of the orga-
nization.
This approach reflects a crucial principle we’ve observed
across successful transformations: management vision must
be rooted in practical understanding if it is to be successful in
motivating and persuading employees. When executives spend
time actually working with AI agents, even for just a few hours,
they develop an intuitive grasp of the technology’s capabilities
and limitations. This understanding leads to more realistic and
achievable transformation goals, which can be more easily
communicated and applied to the specific situation of the
workforce.

The Power of Leading by Example

Perhaps the most underappreciated aspect of management’s
role in AI agent transformation is the importance of leading by
example. We’ve consistently observed that when executives
actively use and champion AI agents in their own work, adoption

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throughout the organization increases dramatically. An important

reason is that the leadership will be seen as more credible and
therefore accepted as legitimate in proposing and introducing
AI agents in the organization. This phenomenon played out
powerfully at a consulting firm where we helped implement AI
agents across their operations.
The Managing Partner made a conscious decision to inte-
grate AI agents into his daily work, using them openly during cli-
ent presentations and internal meetings. He would demonstrate
how he used agents to analyze data, generate insights, and draft
preliminary recommendations. This transparency accomplished
two crucial things: it demystified the technology for others in the
organization, and it sent a clear, credible message that AI agents
were tools that the organization and its members should embrace
instead of fear.
The impact was remarkable. Within six months, the firm saw
a 300% increase in voluntary adoption of AI agents across all
levels of the organization. When we interviewed employees about
this rapid adoption, many cited the Managing Partner’s example
as a key factor in their decision to embrace the technology.

Understanding the Power of Enterprise-Wide

Transformation
The implementation of AI agents represents more than just a
technology upgrade—it’s a fundamental transformation in how
work gets done across an organization. Through our research
and hands-on experience, we’ve found that companies who
apply AI agents across multiple functional areas are 2 to 3 times
more likely to succeed in their transformation efforts than those
who limit implementation to isolated pockets.191

191
McKinsey & Company, 2024. “Gen AI in Corporate Functions: Looking
Beyond Efficiency Gains,” https://www.mckinsey.com/capabilities/
operations/our-insights/gen-ai-in-corporate-functions-looking-beyond-
efficiency-gains

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Our experience consistently shows that organizations taking

an enterprise-wide approach to AI agents are more likely to
succeed in their transformation efforts than those limiting
implementation to isolated departments. This stark difference
stems from several key factors that we’ve observed across
successful implementations.
Most end-to-end business processes cut across functions;
the order-to-cash process, for example, involves sales, finance,
manufacturing, supply chain and logistics, and multiple
information systems that support those functions. Implementing
agents that support such processes may be difficult and time-
consuming, but it is the only way to achieve major benefits in
terms of productivity gains and customer satisfaction.
Let’s explore how this played out at a global manufacturing
company we worked with. Initially, they planned to implement
AI agents only within their finance department for invoice
processing.
We asked them to explain how they saw this application
aligning with the vision and purpose of the company. Using their
answer, we then asked whether they could see more AI agent
applications across the organization that would equally serve the
vision and purpose of the organization. Through our guidance
and questioning, ultimately, the company expanded its adoption
strategy across the entire organization, thereby increasing
the likelihood of creating real value across the board and all
stakeholders. This approach to guide their adoption journey
illustrates the power of thinking enterprise-wide from the start.

The Value of Comprehensive Scope

The company began by mapping interconnections between
departments and quickly realized a transformative insight: the
traditional approach of managing processes within isolated
silos was holding them back. For example, an invoice arriving
in finance was more than just a financial transaction. It rippled
across procurement, vendor management, customer service, and

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logistics. Procurement needed to verify deliveries, customer

service needed pricing information for customer inquiries, and
logistics required shipping confirmations. These dependencies
revealed that no department operated in isolation—yet, the
systems supporting them were designed as if they did.
And these insights were doing the trick. As the company
leaders—under our guidance—understood that AI agents could
only create real value when a cooperative partnership could
be installed (between humans and AI and among AI agents
themselves), they quickly realized that they needed to promote
cross-functional collaborations and consequently eliminate
the practice of working in siloes that had slipped into the
organization’s mindset over the years.
So, instead of creating multiple agents—one for finance,
another for procurement, and so on—the company took a bold
step. They recognized that building isolated agents for each
department would only replicate the very silos they were trying
to overcome. Such an approach would limit the transformative
potential of AI, merely automating fragmented processes without
addressing the bigger challenge: creating seamless, end-to-end
workflows across the enterprise.
To break free from these constraints, they designed trans-
verse, end-to-end agentic systems capable of operating across
departments. These agents didn’t just automate individual tasks
within a single function; they orchestrated entire workflows that
spanned multiple areas; for example, rather than having a finance
AI agent pass information to a separate procurement AI agent, a
single end-to-end agent was created to handle everything from
invoice receipt to payment processing. This agent could validate
deliveries with procurement, confirm shipping with logistics, and
flag pricing inconsistencies for customer service—all within one
cohesive system.
This cross-functional approach unleashed capabilities that a
siloed implementation could never have achieved. By cutting
across traditional boundaries, the AI agents began surfacing

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insights that no single department could have identified. For

instance, patterns emerged linking payment terms to vendor
performance and cash flow, enabling the company to optimize
vendor selection, negotiate better contracts, and improve
liquidity. These insights not only improved operations but also
enhanced strategic decision-making.
The results were transformative: a 40% reduction in processing
time and a 25% improvement in cash flow management. But the
real breakthrough was cultural. By implementing AI in a way
that transcended departmental divides, the company shifted from
a fragmented organization to a unified, data-driven enterprise.
The AI agents became catalysts for collaboration, breaking
down barriers and fostering a mindset where solving problems
collectively took precedence over protecting departmental turf.
This experience underscores a crucial lesson: the power of
agentic AI lies not just in its ability to automate but in its capacity
to integrate. Organizations should resist the temptation to deploy
multiple isolated agents and instead seize the opportunity to
design systems that cut across silos. True transformation happens
when AI becomes the glue that binds the enterprise, enabling it
to operate as a cohesive, intelligent whole. This approach asks
for building multi-agent systems where different agents play
different roles that have to be coordinated across the board by
human leaders who guide this collaborative dynamic based on
the goal and vision of the organization.

Economies of Scale and Investment Optimization

The enterprise-wide approach also delivered significant econ-
omies of scale. The company could negotiate better terms with
technology vendors, share development costs across depart-
ments, and build a centralized support infrastructure. More im-
portantly, lessons learned in one area could be quickly applied
to others, accelerating the overall transformation with the extra
bonus that best cases were developed that would prove useful in
any future transformation project.

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Their investment in core AI agent infrastructure—including

security frameworks, data management systems, and integration
platforms—could be amortized across multiple use cases.
This made individual department implementations more cost-
effective and easier to justify.

Building an Enterprise-Wide Business Case

A crucial element of their success was developing a comprehensive
enterprise-wide business case. This went beyond traditional
department-level ROI calculations to consider cross-functional
benefits and network effects. The business case included:

• Direct cost savings and efficiency gains in each

department
• Cross-functional benefits from improved data sharing
and process integration
• Reduced technology costs through economies of scale
• Improved customer experience from end-to-end process
optimization
• Enhanced employee satisfaction from the elimination of
repetitive tasks across all functions

This enterprise-wide view helped secure even broader

executive support by demonstrating the full potential value of
the transformation. It also provided a framework for prioritizing
implementations and allocating resources across departments.
Taking the enterprise view of agents may also require new
organizational structures and leadership roles. Most companies
don’t have cross-functional process owners or anyone who can
make decisions for the end-to-end process. Ideally, implementing
new agentic architectures would involve the creation of process
owner roles, but this is unlikely. Most organizations will,
however, need councils or representatives to deliberate over
cross-functional agents. Leaders should encourage their heads

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of functions and units to collaborate on enterprise agent projects

and get involved themselves at times.

Building a Comprehensive Business Case

The business case for AI agents needs to go beyond traditional
return on investment calculations. Leaders need to encourage
stakeholders of agent-based initiatives to take a broader
perspective than they might otherwise adopt. When we worked
with a major insurance company, their initial business case
focused solely on cost savings from automation. This narrow
view led to resistance from middle management, which saw the
initiative as purely cost-cutting. We helped them rebuild their
business case to include four key components that we’ve found
essential for AI agent transformations:
First, quantitative benefits: This project involves not just
cost savings but also revenue enhancement opportunities. For
example, their AI agents could handle 40% more customer
inquiries, leading to a 15% increase in policy sales. They also
measured improvements in processing speed, error reduction,
and compliance accuracy.
Second, qualitative benefits: This category of benefits en-
compasses improved employee satisfaction (as agents handle
routine tasks), better customer experience (through 24/7 avail-
ability and consistent service), and enhanced operational resil-
ience. The insurance company found that employee satisfaction
scores increased by over 30% when AI agents took over routine
claims processing.
Third, implementation costs: The costs of production
implementation and deployment should include not just
technology costs but also change management, training, and
potential productivity dips during the transition period. The
insurance company allocated 40% of its budget to change
management and training, which proved crucial for successful
adoption.

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Fourth, risk assessment and mitigation: Risks from the project

include both technical risks (like system integration challenges)
and organizational risks (such as employee resistance or skill
gaps). They identified critical risks like data security concerns
and developed specific mitigation strategies for each.

Governance for AI Agent Implementations: A

Holistic and Ethical Approach
Effective governance for AI agent implementations must
balance the need for innovation with the imperative for control
while embedding ethical principles into every layer of decision-
making. This requires a structured yet flexible framework that
ensures alignment across the organization, fosters innovation,
and maintains public trust. As with other forms of AI, ethical
governance is not a matter of policies alone but rather the
evaluation of use cases from the time they are conceived.192
A standout example of overall governance and ethics for
agentic initiatives comes from a global bank that implemented
a comprehensive governance model centered on an “Agent
Innovation Council.” This council unified business unit leaders,
IT executives, and employee representatives to create a shared
governance framework. Its key strength lies in integrating strategic
oversight with operational flexibility, allowing innovation to
flourish without sacrificing control.
The council operated through a three-tiered structure:

• At the top, a steering committee set the strategic direction,

ensuring all AI initiatives aligned with broader company
objectives. This committee also managed resource
allocation, ensuring that high-priority projects received
adequate support.

192
Thomas Davenport and Randy Bean, 2023. “AI Ethics at Unilever: From
Policy to Process,” https://sloanreview.mit.edu/article/ai-ethics-at-unilever-
from-policy-to-process/

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 Agentic Artificial Intelligence

• The second tier, a Center of Excellence (CoE), acted

as the operational hub, providing technical expertise,
standardizing implementation practices, and ensuring
compliance with governance protocols.

• At the foundational, individual departments designated

“AI Champions” to identify use cases, advocate for
adoption, and act as liaisons between business units and
the CoE. With AI champions actively present across
teams, conversations about how to use AI effectively
became a natural part of daily operations, fostering a
bottom-up culture of experimentation and innovation.
Instead of governance being perceived as bureaucratic
red tape, it became a supportive structure that encouraged
responsible AI experimentation.

Crucially, the governance framework also emphasized

ethical oversight as an integral part of the structure. The same
global bank established an “AI Ethics Board” to review all major
AI implementations. This board was responsible for upholding
core ethical principles such as data privacy, decision-making
transparency, fairness, and accountability.
By embedding ethics into the governance structure, the
organization avoided relegating these considerations to a
secondary role. Instead, ethical review became a mandatory
checkpoint early in the AI development lifecycle, allowing for
the possibility of revising the use case, reinforcing public trust,
and mitigating potential regulatory risks.
For example, the ethics board established guidelines that
ensured AI agents operated within clear boundaries regarding
data usage. These guidelines prohibited practices such as biased
decision-making in automated hiring processes or opaque
pricing algorithms.
The bank went beyond internal oversight and created a direct
feedback loop with customers. The real-time feedback system

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allowed customers to flag concerns immediately when they

noticed inconsistencies, unfair decisions, or potential bias in AI-
driven processes. Whether it was an automated loan approval,
fraud detection, or customer service response, users could report
discrepancies, prompting an internal review. By proactively
addressing ethical concerns, the bank not only safeguarded its
reputation but also enhanced employee and customer confidence
in its AI initiatives.
The monthly meetings of the Agent Innovation Council
became the linchpin of this governance model. These sessions
provided a forum to evaluate progress, address concerns, and
ensure alignment with the company’s evolving strategy. By
fostering collaboration across departments and maintaining an
open channel for feedback, the council created a culture where
innovation thrived alongside accountability.
This integrated governance model demonstrates that
effective AI transformation is not just a technical endeavor—it
is a reimagining of “how” work is done across the organization.
By aligning strategic oversight, operational support, and ethical
accountability, the governance framework not only ensured the
smooth implementation of AI agents but also solidified their role
as a force for long-term organizational value.
A common concern about governance-heavy AI initiatives
is that they introduce excessive bureaucracy, slowing down
innovation. However, in this case, the opposite happened. The
presence of AI Champions across departments created a culture
where AI use was constantly discussed, refined, and optimized.
Instead of AI being a distant, IT-controlled system, employees
were actively engaged in shaping how AI could improve their
daily work.
This cultural shift led to grassroots innovation. For instance,
one team in the bank’s risk management division, inspired by
internal AI discussions, proposed an AI-powered early-warning
system for fraud detection. Because the governance structure
allowed for rapid evaluation and testing, this idea moved from

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concept to implementation faster than traditional innovation

pipelines.
This approach underscores again the importance for
organizational leadership to be participating from the start of the
adoption process as their early presence introduces an immediate
focus on how important the company’s goals, values, and purpose
are for any change and transformation project the organization
engages in. As such, ethics and governance will, in an organic
manner, become the glasses that leaders and employees will use
to look at and evaluate the integration of AI agents in successful
ways.
It leaves little doubt that organizations embarking on
AI transformations must adopt this holistic approach, as it
will position and recognize governance as both an enabler of
innovation and a safeguard against ethical missteps. When
governance frameworks are designed to integrate ethics deeply
into their structure and are visible and directing from the very
beginning of the adoption project, they will not only guide the
technology but also shape its effectiveness to enhance human
work conditions and contribute to the benefits and interests of
all stakeholders.

Key Performance Indicators and Monitoring

Key Performance Indicators (KPIs) and monitoring are essential
for assessing the success of an AI agent transformation. KPI’s are
familiar to most managers when it involves assessing whether
employees perform successfully. However, when it comes down
to assessing the success of adopting AI agents with respect to the
value the company generates, many companies fail.
In fact, it is our experience that today, most companies
hardly have clear measures in place to assess whether their
AI adoption efforts have been successful. The reason for this
is that companies usually start out by implementing a few AI
agent experiments that are run in siloed ways. They do so to
get a feel of the technology, but in this process, they pay little

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or no attention to thinking about what makes an AI adoption

successful (see the lack of a holistic approach). It’s thus crucial
to establish clear metrics at the outset and continuously monitor
them to guide the transformation journey. By focusing on a single
example throughout, we can provide a practical framework for
tracking progress.
Consider a financial services firm implementing AI agents
to streamline operations and improve customer support. Before
deployment, the company established baseline metrics across
four critical areas: operational efficiency, employee impact,
customer experience, and agent learning. These diverse domains
effectively comprise a “balanced scorecard” for the success and
value of agent-oriented projects.
Operational metrics measured processing times, error rates,
and costs per transaction. At the project’s start, processes were
slow and error-prone. After implementing AI agents, the firm saw
a 60% improvement in process efficiency and an 85% reduction
in errors. These gains directly aligned with the company’s goal
of reducing operational costs while increasing accuracy.
Employee impact metrics assessed how AI agents
augmented staff roles. The firm tracked time saved on routine
tasks and surveyed employees about job satisfaction and skills
development. Employees reported spending 60% more time
on strategic activities and a significant boost in satisfaction as
repetitive tasks were automated, enabling them to focus on more
meaningful work.
Customer experience metrics were another key focus, tracking
satisfaction scores, resolution times, and service availability.
By deploying AI agents for first-line customer support, the
firm achieved a 30% increase in customer satisfaction. Faster
resolution times and 24/7 availability played a significant role in
enhancing the customer experience.
Finally, learning and adaptation metrics monitored the
agents’ ability to improve over time. Initially, AI agents could
handle 40% of customer cases autonomously. Six months later,

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this figure rose to 75% as the agents adapted to handle more

complex scenarios, reducing the need for human intervention.
The firm used these metrics not only to measure success
but also to maintain a feedback loop. Monthly reviews allowed
them to track progress, identify areas for improvement, and
adjust their strategy. For example, when monitoring showed that
agent performance plateaued in handling high-priority cases,
the company provided additional training data and fine-tuned
the algorithms. This iterative approach ensured continuous
improvement.
By tying all metrics back to the original business case, the
firm maintained alignment with its strategic goals. Clear KPIs,
OKRs, growth indicators, and consistent monitoring gave
stakeholders confidence in the value of holistic transformation
and sustained momentum throughout the journey. This example
illustrates how a structured, data-driven approach can make AI
agent transformations measurable, actionable, and evaluative in
terms of progress at a holistic level—encompassing both human
growth and AI integration—ultimately leading to success.

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CHAPTER 11
SCALING AI AGENTS:
FROM VISION TO REALITY

The Right Scaling Approach

From Rules to Reasoning: Scaling AI Agents
in the Enterprise

T
he conference room fell silent as we displayed the final
numbers from our pilot implementation. The Level 3 AI
agent had successfully processed over 10,000 customer
service requests in just two weeks, achieving an accuracy rate
that exceeded our human baseline. The executive team leaned
forward, clearly impressed. Then came the question we’d been
anticipating: “Great results. How do we scale this across the
entire organization?”
This scene, which unfolded at a Fortune 500 insurance
company we worked with last year, highlights both the immense
potential and the inherent challenges of scaling AI agents across
an enterprise. While individual pilot successes are increasingly
common, very few organizations have managed to extend their
AI agent implementations beyond isolated use cases.

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Our research reveals a striking statistic: fewer than 1%

of companies piloting Level 3 AI agents successfully deploy
them at scale. This mirrors the situation we encountered 7–10
years ago with the emergence of intelligent automation (Level
2 agents). At that time, organizations faced similar hurdles in
scaling their initiatives.
Given that no company has yet established a proven
framework for scaling Level 3 AI agents, we will draw from past
experiences and current, albeit limited, observations to outline
the optimal approach.

The Foundation: Understanding Where to Start

When we first began working with this global insurance company,
the leadership team was eager to jump straight into deploying
AI agents across their entire claims processing operation.
Their enthusiasm was understandable—the pilot results were
compelling. However, we’d seen this movie before.
“Before we talk about scaling,” we told them, “let’s discuss
what happened with your RPA implementation three years ago.”
The CIO shifted uncomfortably in his chair. Their previous
attempt to scale automation had stalled after initial successes,
leading to what one executive called “a graveyard of broken
bots.”

Finding the Right Opportunities

Rather than making assumptions from the top down, we took a
grassroots approach, spending six weeks embedded with various
departments across the organization. “We need to understand
where people are actually spending their time, not where we
think they’re spending it,” we explained to the executive team.
We conducted structured interviews with over 50 team leaders
and employees across claims, customer service, underwriting,
and operations. These weren’t just formal meetings—we sat
with employees, observed their daily work, and listened to their

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frustrations. One claims adjuster’s comment particularly stuck

with us: “I spend about four hours each day just gathering and
organizing information from different systems before I can even
start analyzing a claim.”
This kind of insight proved invaluable. Through these
conversations and observations, we created detailed workload
maps for each department, identifying where time was being
spent and, more importantly, where it was being wasted. We
discovered that employees across departments were spending
60-70% of their time on what they described as “administrative
overhead” rather than value-added work.

Prioritizing for Impact

With our workload mapping complete, we applied what we
call the “20/80 principle”—identifying the 20% of activities
consuming 80% of people’s time. These high-workload activities
became our primary focus for AI agent implementation.
In the claims department, for example, we found three
activities that consumed disproportionate amounts of time:

• Information gathering and consolidation from multiple

systems
• Initial claims triage and routing
• Standard correspondence with customers and providers

These activities weren’t just time-consuming—they were

also described by employees as repetitive, tedious, and prone to
errors. “It’s mind-numbing work,” one team member told us. “I
went to school for insurance adjusting, but I spend most of my
day copying and pasting information.”

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Feasibility Assessment
Not every time-consuming process is suitable for AI agent
automation. We evaluated each opportunity against three key
criteria:

• Technical feasibility: Could current AI agent technology

handle the task effectively?
• Process stability: Was the process stable enough to
automate, or did it require significant redesign?
• Data availability: Did we have access to the necessary
data in a usable format?

This assessment led us to an important realization: many

of our high-workload processes were interconnected through
shared data and systems. The information gathering that claims
adjusters struggled with used many of the same data sources that
underwriters accessed for policy renewals.

Building the Business Case for Scale

The insights we gathered during our six-week assessment
phase didn’t just help us identify opportunities—they provided
the foundation for a compelling business case. “Data tells the
story,” we explained to the executive team. “And our data tells
us exactly where and how AI agents can create the most value.”
The data gathered from our workload mapping and feasibility
assessments formed the foundation of our business case. We
structured it around both quantitative and qualitative benefits.
On the quantitative side, we calculated that automating the
identified high-workload processes would save approximately
45,000 person-hours annually across the claims department
alone, translating to $3.2 million in direct cost savings. The
qualitative benefits were equally compelling: reduced error
rates, faster claims processing, improved customer satisfaction,

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and enhanced employee engagement through the elimination of

repetitive tasks.
We also factored in implementation costs, including tech-
nology licenses, development resources, and change manage-
ment efforts, arriving at an expected ROI of 285% over three
years. This comprehensive business case proved crucial in
securing executive buy-in and necessary funding for the full-
scale implementation. More importantly, it established clear
metrics for success that we could track throughout the imple-
mentation, helping us maintain momentum and demonstrate
value to stakeholders at each phase of the rollout.

The Systematic Path to Scale

With our priority opportunities identified, we established a
structured three-phase approach for implementing each AI agent
in the company. Rather than tackle everything at once, we moved
methodically through process redesign, deployment sprints, and
production migration. Let’s see how this worked with our first
major implementation—the claims information gathering and
consolidation agent.

Phase 1: Process Redesign and Optimization

“If we’re going to automate this, let’s make sure we’re automating
the right process,” became our mantra. Working with a cross-
functional team of claims adjusters, IT specialists, and process
excellence experts, we spent three weeks mapping the current
process in detail. The findings were eye-opening—adjusters
were accessing seven different systems to gather information,
often in a redundant manner.
We reimagined the process from scratch, asking ourselves:
“If we were building this today with AI agents, how would it
work?” The redesigned process consolidated access points,
standardized data formats, and created clear handoffs between AI
agents and human adjusters. We reduced the system touchpoints

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from seven to three by implementing a centralized data lake that

the AI agent could query directly.

Phase 2: Deployment Sprints

With our redesigned process in hand, we moved into the
development phase, organizing our work into two-week sprints.
Each sprint focused on delivering specific functionality that
could be tested and refined. For the claims information agent,
we structured six sprints, each building upon the previous one’s
functionality.
We involved claims adjusters in every sprint review,
gathering their feedback and making adjustments. This rapid
iteration cycle proved invaluable. During the third sprint, for
instance, adjusters pointed out that certain claim types required
special handling—something we hadn’t initially considered. We
quickly adapted the agent’s logic to account for these exceptions.

Phase 3: Testing and Production Migration

Rather than a “big bang” approach, we implemented a graduated
deployment strategy:

• Week 1: 10% of incoming claims routed through the AI

agent
• Week 2: 25% of claims, with expanded monitoring
• Week 3: 50% of claims, with reduced manual oversight
• Week 4: 75% of claims, maintaining audit protocols
• Week 5: Full deployment with standard monitoring

This gradual approach allowed us to build confidence while

managing risk. “At first, I kept double-checking everything the
agent did,” one senior adjuster told us. “But after a few weeks, I
trusted it more than I trusted my own data gathering.”

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 Scaling AI Agents: From Vision To Reality

The Results and Lessons Learned

Within six months, we had successfully scaled AI agents across
multiple processes, achieving significant results:

• Reduced information-gathering time by 70%

• Automated 85% of standard customer correspondence
• Cut claims processing time by 45%

More importantly, employee satisfaction improved signifi-

cantly. “For the first time in years, I’m spending most of my day
actually analyzing claims instead of just gathering information,”
reported one senior adjuster.
The key lessons from our journey? Success with AI agents
isn’t about implementing the most advanced technology—
it’s about finding the right opportunities where automation
can create the most value. By taking a systematic approach to
implementation and scaling, organizations can create sustainable
value while avoiding the common pitfalls that derail many
automation initiatives.
As we look to the future, we continue to evolve our
approach. The technology landscape is rapidly changing, with
new capabilities emerging regularly. However, the fundamental
principles remain the same: start with the right opportunities,
redesign processes thoughtfully, implement systematically, and
always keep the human element in mind.
As one team leader at the global insurance company put it,
“These AI agents aren’t replacing us; they’re finally letting us
do the job we were hired to do.” That, perhaps, is the ultimate
measure of success in scaling AI agents.

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The Automation Experience Advantage:

from Level 2 to Level 3 Agents
In the landscape of artificial intelligence, the journey from basic
automation to sophisticated AI agents isn’t always a straight
line. Some organizations are discovering that their previous
investments in automation technologies are becoming unexpected
stepping stones to more advanced AI implementations. To
understand this evolution, let’s explore the experience of
Johnson Controls International (JCI), a global leader in building
technologies and solutions, as they navigate this transformation.

The Foundation: From Automation to Intelligence

When we first met with Ramnath Natarajan, who leads global
implementation of operational excellence and intelligent
automation at JCI, we were struck by the scope of their existing
automation infrastructure. “We currently operate with 250
digital workers and 2,000 APIs,” he explained, highlighting
a sophisticated Level 2 (Intelligent Automation) automation
environment that combines robotic process automation (RPA),
business process management (BPM), and various AI tools.
This foundation might seem like a complex technical de-
tail, but it represents something more fundamental: an or-
ganization-wide commitment to process improvement and
digital transformation. For companies like JCI, this existing
infrastructure isn’t just about cost savings—though they’ve
achieved significant financial benefits. It’s about having built
the organizational muscles needed to implement and scale au-
tomated solutions.

The Catalyst for Change

What makes JCI’s story particularly interesting is their
recognition that while their Level 2 intelligent automation systems
were delivering value, they were hitting natural limitations.

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Traditional automation approaches excel at handling structured,

predictable tasks but struggle with the kind of adaptive, context-
aware operations that modern businesses increasingly require.
“We’re moving beyond task-specific automation to agents
capable of orchestrating entire workflows,” Natarajan shared
during our discussion. This shift represents the core difference
between Level 2 and Level 3 agents. While Level 2 systems can
handle complex but predetermined scenarios, Level 3 agents can
understand context, process natural language, and orchestrate
multiple tools to achieve broader objectives.

The Bridge Between Levels

Level 2 automation, characterized by tools like RPA and
intelligent automation, excels at handling repetitive, rule-based
tasks within clearly defined parameters. Level 2 systems, with
their reliance on predefined rules and structured data, are like
highly efficient assembly line workers. They’re excellent at
their specific tasks but can’t adapt to significant changes without
human intervention.
For example, JCI used RPA to streamline billing processes,
significantly reducing manual errors and improving efficiency.
However, even with these gains, human oversight remained
a critical component. These systems struggled to adapt to
dynamic, cross-functional workflows or manage exceptions
that required nuanced decision-making. This limitation became
evident in areas such as customer service and field operations,
where delays and inefficiencies often resulted from fragmented
processes and reliance on human intervention.
JCI’s progression to Level 3 AI agents was designed to
address these gaps. Natarajan emphasized the shift in strategy:
“We’re moving beyond task automation to agents that can
orchestrate entire workflows. The focus is on eliminating human
dependencies and achieving seamless process execution across
functions.” This vision required reimagining automation as

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an interconnected, adaptive system rather than a collection of

isolated tasks​
JCI’s journey to Level 3 automation illustrates how AI agents
transform existing intelligent automation systems into powerful,
adaptive ecosystems. The transition is not about replacing
intelligent automation but enhancing and connecting it. By
starting with targeted applications—like managing exceptions
or handling nuanced decisions—AI agents prove their value
incrementally, building trust and paving the way for broader
deployment.
This approach enables organizations to achieve not only
operational efficiency but also strategic agility. AI agents
eliminate bottlenecks, reduce reliance on human intervention,
and empower teams to focus on high-value activities. As
Natarajan succinctly put it: “It’s not just about automation; it’s
about creating a system that adapts and thrives on its own”​. JCI’s
success demonstrates that the leap to Level 3 is both achievable
and transformative.

The Advantage of Experience

What makes JCI’s story particularly instructive is how their
experience with Level 2 automation created advantages in
implementing Level 3 agents. Several key factors contributed
to this:

1. Infrastructure Readiness: Their existing network of APIs

and digital workers provided a ready-made foundation for
more sophisticated agents to build upon. This meant they
weren’t starting from scratch but rather extending and
enhancing existing capabilities.

2. Process Understanding: Years of implementing auto-

mation had given them deep insights into their business
processes and where human intervention was most cru-
cial. As Natarajan noted, their goal became to “replace

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human-in-the-loop dependencies with fully autonomous,

process-driven agents.”

3. Organizational Alignment: Perhaps most importantly,

they had already established the organizational structures
needed to implement and scale technological solutions.
Their team of over 100 employees dedicated to operational
excellence and automation provided the human expertise
needed to guide this evolution.

Learning from Challenges

The transition hasn’t been without its challenges, and these
difficulties offer valuable lessons for other organizations
considering similar journeys. JCI encountered several significant
hurdles:
Integration Complexity: Even with their extensive experience,
connecting agents with legacy systems proved challenging.
“Grounding data by region is complex, given the organization’s
heterogeneous systems,” Natarajan explained. This highlights how
even organizations with mature automation capabilities need to
carefully consider their technical architecture when moving to more
sophisticated agents.
Vendor Limitations: JCI found that their existing automation
vendors weren’t necessarily fully ready for more advanced agent
implementations. They discovered that some vendors’ lack of
deep integration capabilities slowed agent development, while
some other vendors’ broad agent definitions created alignment
challenges. This experience underscores the importance of carefully
evaluating technology partners for Level 3 implementations,
regardless of existing relationships.

The Path Forward

JCI’s experience suggests that while previous automation expe-
rience can provide advantages in implementing Level 3 agents,

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it’s not a prerequisite. The key is understanding that Level 3

agents represent a fundamentally different approach to automa-
tion—one that requires careful consideration of both technical
and organizational factors.
Their vision for the future includes having multi-agent
systems operational by 2026, highlighting how this evolution is
part of a longer-term digital transformation journey. “Agents will
execute end-to-end processes, integrate seamlessly, and enhance
business autonomy,” Natarajan shared, outlining a future where
AI agents become integral to business operations.

Key Considerations for Organizations

For organizations considering their own journey toward Level 3
agents, several insights emerge from JCI’s experience:
Start with Clear Use Cases: JCI’s focus on customer service,
field operations, corporate finance, and procurement provided
clear, value-driven targets for agent implementation. This
focused approach helps manage complexity and demonstrate
value.
Build on Strengths: Whether those strengths come from pre-
vious automation experience or other organizational capabilities,
identify and leverage existing advantages rather than starting
completely fresh.
Manage Expectations: The jump from Level 2 to Level 3
isn’t just a technical upgrade—it represents a fundamental shift
in how work gets done. Organizations need to prepare for both
the opportunities and challenges this presents.

The Broader Perspective

While JCI’s story demonstrates how previous automation
experience can facilitate the implementation of Level 3 agents,
it’s important to note that this isn’t the only path forward.
Organizations without extensive automation experience can still

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successfully implement Level 3 agents by focusing on clear use

cases and building the necessary organizational capabilities.
The key is understanding that Level 3 agents represent a
significant advancement in how automation can serve business
needs. These agents’ ability to understand context, process
natural language, and orchestrate complex workflows offers
opportunities for transformation that go beyond traditional
automation approaches.
As we look to the future, the evolution of AI agents will
continue to reshape how organizations operate. Whether
building on existing automation capabilities or starting fresh
with more advanced implementations, the key to success lies in
understanding both the technical capabilities and organizational
changes required to effectively leverage these powerful tools.

Leveraging Generative AI and AI Agents

for a Holistic AI Corporate Transformation
As we sat in a conference room in Singapore with the executive
team of a global manufacturing company, an interesting debate
unfolded. The CTO was adamant about building AI agents
to automate their operations, while the Head of Innovation
passionately advocated for empowering employees with
generative AI tools. Little did they know that this apparent
tension would lead us to one of our most fascinating discoveries
about the future of work.
What we witnessed over the next six months became a perfect
laboratory for understanding how AI transforms organizations.
Not through one pathway, but through two complementary
forces that, when combined thoughtfully, create something far
more powerful than either could achieve alone.
Let’s start with an experiment we conducted with their sales
team. We asked half the team to continue working as usual,
while the other half got access to both generative AI tools and a

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basic AI agent we had developed. The results were striking, but

not for the reasons we expected.
Deborah, one of their top performers, found herself relying
on an AI agent to quietly handle her meeting scheduling, follow-
ups, and pipeline updates in the background to free-up her time.
But what really caught our attention was how she used her newly
freed time. She leveraged generative AI to craft more persuasive
proposals and personalized client communications. “I’m
finally able to do what I always wanted,” she told us. “Instead
of spending hours on the logistics of organizing meetings and
struggling to strategically think about my client deals, I’m
having more and more deep conversations with clients about
their long-term challenges.”
This experience illustrated what we’ve come to understand
as the dual transformation that AI enables in organizations. Think
of it as learning to drive with both an automatic transmission
and a GPS. The automatic transmission (like AI agents) handles
the mechanical complexity, while the GPS (like generative AI)
enhances your ability to navigate and make better decisions.
Together, they don’t just make you a more efficient driver—they
transform the entire experience of traveling.
The fusion of generative AI and AI agents presents a
transformative opportunity for organizations. We’ve discovered
that this dual approach creates something greater than the sum of
its parts. Together, these technologies not only redefine the way
companies operate but also empower employees to embrace their
most human qualities—creativity, empathy, critical thinking,
and personal growth. This dual approach fosters a workforce
capable of achieving exceptional outcomes while redefining the
future of work.

The Dual Role of AI in Corporate Transformation

AI’s transformative potential can be broadly categorized into
two interdependent pathways.

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The first is Generative AI, a technology that enhances

human capabilities by enabling employees to connect with
others, think critically, and create with unprecedented speed
and depth. Generative AI tools transform tasks such as drafting
communications, creating content, or analyzing complex data
into moments of empowerment. With these tools, employees
become “superhumans,” equipped to amplify their natural
strengths and tackle challenges previously beyond their reach.
The second pathway is through AI Agents, systems designed
to handle repetitive, complex, sensitive, or even hazardous
tasks. These agents automate processes, saving time and
reducing errors while managing tedious responsibilities. By
taking on these roles, AI agents free employees to focus on more
meaningful activities, whether those activities involve strategic
contributions in their professional roles or pursuing personal
goals and enriching experiences in their lives. Together, these
technologies allow companies to achieve two goals: scaling
efficiency while unlocking the full human potential of their
workforce.

Strengthening Human Qualities with Generative AI

According to our experience, generative AI has emerged as a
democratizing force, enabling employees to perform tasks
more effectively and creatively. Unlike previous waves of
AI, generative AI actively complements human intelligence.
We’ve seen, for example, how a marketing professional using
generative AI can produce engaging campaigns faster, while a
manager can analyze performance data with greater nuance to
make smarter, faster decisions.
A paradox we’ve observed is that while over 90% of
companies cautiously experiment with generative AI, 75% of
knowledge workers globally already use it daily, with many
resorting to unsanctioned tools. This grassroots adoption
demonstrates the intuitive appeal of generative AI—it’s simple
to use, delivers immediate results, and integrates seamlessly

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into workflows. Employees are outpacing their employers,

showcasing a bottom-up transformation driven by individuals
rather than corporate mandates.
This shift underscores the need for companies to embrace
generative AI at scale. By providing structured training, open
forums for sharing use cases, and platforms for collaboration,
businesses can channel this organic adoption into measurable
improvements. For instance, we’ve seen organizations host
weekly sessions where employees share how they use generative
AI, creating a culture of collective learning and innovation.
Generative AI doesn’t just save time—it creates time for
higher-value activities. We’ve worked with teams where a
product designer uses AI to rapidly prototype ideas, reserving
more time for brainstorming and creative exploration. A sales
team, unburdened by the manual drafting of emails, can instead
focus their energy on deepening client relationships. Generative
AI is not about replacing humans; it’s about elevating them.

Automating Complexity with AI Agents

According to our experience, AI agents operate as silent partners
in corporate transformation, excelling in areas that demand
precision, speed, and reliability. We’ve seen them handle
repetitive tasks such as invoice processing, logistics coordination,
or compliance checks, often working across organizational silos
to optimize processes end-to-end.
For example, in our work with a financial services company,
deploying AI agents to manage billing operations led to dramatic
improvements. Tasks that once required multiple departments—
validating invoices, tracking vendor performance, and
reconciling discrepancies—were seamlessly handled by a single
AI agent. This automation reduced processing time by 40% and
eliminated 85% of errors, freeing employees to focus on strategy
and client engagement.
However, the impact of AI agents extends far beyond
efficiency. By automating the mundane, companies can reallocate

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human effort to areas where creativity, empathy, and decision-

making shine. Employees previously bogged down in repetitive
tasks can instead focus on problem-solving, mentoring colleagues,
or building stronger customer relationships.
From what we’ve observed, this approach requires a strategic,
top-down initiative. Unlike generative AI, which thrives on
individual experimentation, deploying AI agents demands careful
planning, cross-departmental coordination, and robust governance.
Organizations must define clear goals, ensure data integrity, and
design agents that integrate seamlessly into existing workflows to
achieve their full transformative potential.

A Unified Vision: Generative AI and AI Agents

Working Together
Consider our recent work with a global manufacturing company.
When we first started discussing AI transformation with their
leadership team, they were caught in what we call the “either-
or trap”—believing they needed to choose between investing in
generative AI tools for their workforce or developing AI agents
for process automation. What we discovered together was that
the real magic happens when both approaches work in concert.
The true power of AI lies in its integration. Generative AI and
AI agents are not competing technologies but complementary
forces driving a singular vision. Consider an HR department
undergoing the transformation:

• Generative AI helps HR professionals craft personalized

training plans, analyze employee engagement data, and
create impactful communication strategies.

• AI Agents automate routine tasks like processing payroll,

managing benefits, and coordinating schedules.

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Together, these technologies enable the HR team to focus on

what matters most: building relationships, fostering growth, and
creating an environment where employees thrive.
This holistic approach transforms not just workflows but
mindsets. Employees see technology not as a threat but as an
enabler, enhancing their capabilities while allowing them to
engage in activities that bring value and satisfaction.

From Theory to Practice: Building a Transformation

Framework
From our experience, to successfully implement this dual
approach, organizations must adopt a structured transformation
framework:

1. Start with Generative AI: Encourage employees to

experiment with generative AI tools in their daily tasks.
Provide training, incentivize adoption, and create forums
for sharing best practices. This bottom-up strategy builds
excitement and trust in AI while delivering immediate
productivity gains.

2. Scale with AI Agents: Use a top-down approach to deploy

AI agents for automating repetitive and complex processes.
Start with pilot projects to refine the implementation and
gradually scale across departments. Involve employees
in designing agents that complement their workflows,
fostering a sense of ownership.

3. Foster a Culture of Learning and Adaptation: Invest

in continuous education to help employees integrate AI
into their roles. Highlight success stories and encourage
teams to experiment, learn, and innovate.

4. Align Metrics with Strategic Goals: Define KPIs

that measure both efficiency gains and human-centric

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outcomes, such as employee satisfaction, creativity, and

collaboration. Regularly review these metrics to ensure
the transformation stays aligned with organizational
objectives.

5. Prioritize Governance and Ethics: Establish governance

structures that ensure ethical AI use, data privacy, and
transparency. Create forums for employees to voice
concerns and participate in shaping the transformation
journey.

When Agents Go Rogue: Building

Essential Safeguards for AI Systems
In 2010, well before anyone was talking about ChatGPT or
autonomous agents, Wall Street experienced what would later be
called the “Flash Crash.” In just 36 minutes, automated trading
algorithms wiped out nearly $1 trillion in market value. While
the market eventually recovered, this incident offered an early
glimpse of what can go wrong when we give AI agents too much
autonomy without proper safeguards.
This wasn’t supposed to happen. The algorithms were doing
exactly what they were programmed to do—buy and sell based
on market conditions. But their interactions with each other
created an unexpected feedback loop, with bots engaging in
what the Securities and Exchange Commission later described
as a “hot potato” pattern of trading, passing contracts back and
forth at increasingly volatile prices. No single entity was at fault,
yet together, they nearly crashed the market.
We’ve seen this story repeat itself in different ways as AI
agents become more sophisticated and widespread. During
our work with a major financial institution implementing their
first AI-powered trading agents, we made a similar mistake.
The system was designed to optimize trading patterns, but we

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hadn’t properly accounted for how it would interact with other

automated trading systems in the market. Within hours of
deployment, the agent had initiated a series of rapid-fire trades
that, while technically profitable, raised red flags with regulatory
compliance systems. We had to shut it down quickly and reassess
our approach.
These experiences taught us a crucial lesson: When it
comes to AI agents, even well-intentioned designs can lead to
unintended consequences. The challenge isn’t just about making
agents work correctly—it’s about making them work safely and
ethically within a complex ecosystem of human and artificial
actors.

When Good Agents Go Bad

Consider what happened to Air Canada in early 2024. They
deployed a customer service chatbot that was designed to be
helpful and informative. The bot had access to Air Canada’s
website and was instructed to assist customers with their queries.
Simple enough, right? However, things went sideways when the
bot began providing information about bereavement fares that
were far more generous than Air Canada’s actual policy. When
customers tried to claim these fares, Air Canada attempted to
deny them, arguing that the bot’s statements weren’t binding.
They lost that argument in court, with the tribunal ruling that the
company was responsible for their AI agent’s promises.
This incident highlights a fundamental challenge with
AI agents: they can operate independently in ways that their
creators didn’t anticipate and can’t easily control. The Air
Canada bot wasn’t malfunctioning—it was doing exactly what it
was programmed to do: help customers. But its interpretation of
“help” led to commitments that the company hadn’t authorized.
We’ve encountered similar issues in our own work. While
helping a retail client implement an AI agent for inventory
management, we discovered that the agent had begun automatically
placing orders for products that showed declining stock levels.

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On paper, this seemed logical. In practice, it led to massive over-

ordering of seasonal items that were intentionally being phased out.
The agent didn’t understand the broader context of seasonal retail
cycles and inventory strategy.

The New Frontier of Risk

What makes today’s AI agents different from traditional
automation? The answer lies in three key characteristics that
we’ve observed through our implementations across industries:
First, modern AI agents can interpret and act on high-level,
often vague goals. Unlike traditional software that follows rigid
rules, these agents can take a general instruction like “improve
customer satisfaction” and independently determine how to
achieve it. This flexibility is powerful but dangerous—like the
Air Canada bot, they may choose paths that technically achieve
the goal but create other problems.
Second, they can interact with the world in unprecedented
ways. They can access databases, send emails, place orders,
and even control physical systems. One manufacturing client
we worked with implemented an AI agent to optimize their
production line. The agent had the ability to adjust machine
settings in real-time based on quality metrics. While this led
to improved efficiency, it also resulted in occasional sudden
changes that confused and frustrated human operators who
couldn’t predict or understand the agent’s decisions.
Third, these agents can operate indefinitely without direct
supervision. They don’t clock out or take breaks, and they can
continue executing their programmed objectives long after the
original conditions that prompted their creation have changed.
This persistence can be particularly problematic when agents are
operating in dynamic environments where goals and constraints
evolve over time.

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Building Essential Safeguards

Through our experiences and mistakes, we’ve developed a
framework for implementing essential safeguards for AI agents.
These aren’t just theoretical guidelines—they’re practical
necessities that we’ve learned the hard way.

Transaction Management Safeguards

The first line of defense is robust transaction management. This
means implementing clear limits and oversight mechanisms for
any agent that can make financial or resource commitments. We
now always recommend:

• Hard limits on transaction sizes and frequencies

• Multiple approval layers for transactions above certain
thresholds
• Real-time monitoring systems that can detect unusual
patterns
• Clear audit trails for all agent actions

One retail client we work with implemented these safeguards

after their inventory management agent went rogue. Now, any
order above a certain dollar amount requires human approval,
and the system automatically flags unusual ordering patterns
for review. This has prevented several potential over-ordering
incidents while still maintaining the efficiency benefits of
automation.

Ethical Guidelines and Compliance

Ethics can’t be an afterthought in agent design. We’ve learned
to embed ethical constraints directly into agent decision-making
processes. This includes:

• Clear definitions of acceptable and unacceptable actions

• Regular ethical audits of agent behavior

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• Mechanisms for stakeholders to challenge agent

decisions
• Transparency requirements for agent decision-making

The financial institution we mentioned earlier now requires

all their trading agents to undergo regular ethical audits, exam-
ining not just compliance with regulations but also the broader
impact of their trading patterns on market stability.

Safety Controls
Safety safeguards are crucial, especially for agents that control
physical systems or make decisions that affect human safety.
Key elements include:

• Emergency shutdown procedures

• Regular safety checks and validations
• Redundant monitoring systems
• Clear chains of responsibility for safety oversight

A manufacturing client we work with implemented a “human

override” system after several close calls with their production
line optimization agent. Operators can now instantly pause any
agent-initiated changes if they spot potential safety issues.

Privacy Protection
Privacy safeguards have become increasingly critical as agents
handle more sensitive data. Essential protections include:

• Strict data access controls

• Regular privacy impact assessments
• Clear data retention and deletion policies
• Mechanisms for handling personal data requests

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We learned this lesson the hard way when a customer

service agent we implemented began storing detailed customer
interaction logs without proper privacy controls. Now, we
implement privacy protection at the design stage, with clear
policies about what data can be collected and how it should be
handled.

Looking Ahead: The Future of Agent Safety

As AI agents become more sophisticated and widespread, the
importance of these safeguards will only grow. We’re already
seeing the emergence of what we call “agent ecosystems”—
networks of AI agents interacting with each other and with
human systems in increasingly complex ways.
The challenge ahead isn’t just about controlling individual
agents—it’s about understanding and managing these ecosystems.
This requires new approaches to safety and governance that can
handle the emergent behaviors that arise from agent interactions.
We’re currently working with several clients to develop frameworks
for managing these ecosystems, but we’re also humble enough to
acknowledge that we’re all learning as we go.
The future of AI agents is both exciting and daunting. While
these technologies offer tremendous potential for improving
efficiency and innovation, they also present risks that we’re only
beginning to understand. The safeguards we’ve discussed aren’t
perfect, but they represent our best current understanding of how
to harness the power of AI agents while protecting against their
potential dangers.
The key is to remain vigilant and adaptive, learning from
each new challenge and continuously improving our safeguards.
As we often tell our clients, the goal isn’t to eliminate all risks—
that’s impossible. Instead, we aim to create systems that can fail
safely and learn from their mistakes, just as we do.

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CHAPTER 12
CASE STUDY AND USE
CASES OF AGENTS
ACROSS INDUSTRIES

A
s we conclude our exploration of practical AI agent
implementation, this chapter brings together everything
we’ve learned about change management, scaling, and
execution through two powerful lenses. First, we dive deep
into Pets at Home’s transformative journey, which exemplifies
how organizations can successfully implement AI agents at
scale while maintaining their essential human touch. Then,
we examine a diverse collection of use cases across industries
and functions that demonstrate the versatility and impact of AI
agents in real-world settings. These examples aren’t just success
stories—they’re blueprints for your own transformation journey,
offering practical insights and proven approaches that you can
adapt for your organization.

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Case Study: Pioneering Enterprise AI

Agent Transformation: Pets at Home
In the rapidly evolving landscape of enterprise AI transformation,
we’re particularly excited to share the story of Pets at Home,
which we consider one of the global pioneers in implementing
agentic AI at scale. Their achievements speak volumes—
from an ambient digital scribe that transcribes veterinary
consultations with 99.6% accuracy to autonomous agents that
have revolutionized fraud detection across their retail operations.
Under the visionary leadership of Simon Ellis, their Head of AI
Transformation and Enterprise Architecture, the company has
embarked on a first-of-its-kind transformation that is setting new
standards for how enterprises can leverage AI agents.
As the UK’s largest pet care company, with approximately
450 retail stores, 450 veterinary practices, and a comprehensive
grooming service that handles 17,000 pets weekly, Pets at
Home’s journey offers valuable insights into the practical
implementation of enterprise-wide AI agent systems.

The Challenge: Unifying a Complex Enterprise

When Ellis joined Pets at Home, he faced a challenge common to
many large enterprises: operational silos. The company’s various
business units—retail stores, veterinary practices, grooming
services, and online operations—operated independently, creating
disconnected experiences for both customers and employees. With
over 8 million customers in their Pets Club program and data on 10
million pets, Ellis recognized an enormous opportunity to leverage
this information more effectively across their organization.

Strategic Vision: Beyond Simple Automation

What sets Pets at Home’s transformation apart is its bold vision
for transversal AI agents. Rather than viewing AI implementation

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as a series of isolated automation projects, they envision a future

where AI agents become the primary interface between the
company and its stakeholders193. Ellis describes their ambitious
vision: “Our vision is to create an AI digital assistant for each
of our customers... If Pascal comes to Pets at Home, it doesn’t
matter whether you want to research a product, do some online
symptom checking because your pet’s not well, check where
your order is, or manage your subscription—effectively, Pascal
will have a digital assistant that knows you and knows your
pets.”
Ellis further explains how this vision transforms traditional
digital interactions: “These agents, these personalized agents,
are the things that are actually going to transform the retail
market in the next 3 or 4 or 5 years. It’s actually something I
think MDH retail needs to be worried about because you won’t
go to a website anymore.” This represents a fundamental shift in
how companies interact with customers, moving from traditional
channels to personalized AI agents as the primary touchpoint.

Implementation Strategy: Starting Small but

Thinking Big
Pets at Home’s implementation strategy demonstrates several
key principles that we’ve observed as crucial for successful
enterprise AI transformations:

1. Executive Sponsorship and Strategic Alignment:

The transformation began with strong support from the
top, including the chairman, who viewed AI as “the next

193
Personal AI Concierges are expected to be implemented across many
service sectors, see Stephanie Liu, Khadija Ali Vakeel, Nicholas Smith,
Roya Sadat Alavipour, Chunhao (Victor) Wei, Jochen Wirtz (2024), “AI
Concierge in the Customer Journey: What Is It and How Can It Add Value
to the Customer?” Journal of Service Management, Vol. 35, No. 6, 136-158,
https://doi.org/10.1108/JOSM-12-2023-0523.

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industrial revolution.” This executive backing proved

crucial for driving adoption across the organization. Instead
of creating large committees to identify opportunities, they
focused on smaller initiatives with executive sponsors who
were passionate about driving change.

2. Foundation First: Before diving into advanced AI

implementations, the company invested in building a
robust data foundation in Azure. This infrastructure
unified data from various operations, creating a solid base
for AI initiatives. They recognized that both structured
and unstructured data needed to be well-organized for AI
agents to function effectively.

3. Proving Value Through Targeted Pilots: Behind this

unified customer experience, Ellis envisions a network of
specialized agents working together: “Behind the scenes,
there could be a whole bunch of agents—there could be
a veterinary agent, a customer service agent, that could
be a retail sales agent.” Similarly, for employees, “we’re
also building out a colleague assistant” that provides
personalized support based on their role and expertise. For
instance, “if I worked in the store and I was a specialist
in aquarium, it will go ‘Hey Simon, I know you’re a
specialist in aquarium, I know who you are, here’s all the
latest operating procedures.’”

Their first major implementation was an ambient digital

scribe for veterinary practices, which automatically transcribes
consultations and creates clinical notes. This pilot demonstrated
immediate value by standardizing documentation and improving
efficiency, while maintaining human oversight for critical
healthcare decisions.

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Scaling Through Low-Code/No-Code Platforms

One of the most innovative aspects of Pets at Home’s
transformation—and one that particularly resonates with our
experience in the field—is their approach to scaling AI agent
development. Rather than relying solely on traditional software
development, they leveraged Microsoft’s Copilot Studio to
democratize agent creation. Ellis’s enthusiasm is contagious as
he describes building two prototype agents in a single morning
using low-code tools, demonstrating the potential for rapid
deployment and experimentation.
This approach addresses one of the primary challenges in
enterprise AI transformation: the scalability of development.
By enabling subject matter experts to create and modify agents
through low-code interfaces, Pets at Home can accelerate their
transformation while maintaining quality and consistency.

Key Use Cases and Results

Let’s explore some of the most fascinating implementations
we’ve seen at Pets at Home, where AI agents are making a real
difference in day-to-day operations.

Fraud Detection Agent

The autonomous agent for fraud detection beautifully illustrates
the transformative power of AI in tackling complex business
challenges. Kay Birkby, the senior fraud manager, shared
an illuminating example: the agent can spot when the same
photograph of a damaged package is used multiple times by
different people attempting to claim refunds—a pattern that
would be nearly impossible for humans to detect manually.
What’s particularly exciting is how the system goes beyond
simple fraud detection; it also helps identify legitimate product
issues when multiple genuine complaints arise, transforming
fraud detection into a valuable tool for quality improvement.

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Clinical Documentation Assistant

We’re particularly impressed by how the ambient digital scribe
has revolutionized veterinary practice operations. The system
achieves a remarkable 99.6% accuracy in transcription using
standard PC microphones, even with background noise—a feat
that exceeded the team’s initial expectations. But here’s what
really gets us excited: beyond simple transcription, it standardizes
medical coding and documentation across practices, creating
consistency where individual clinicians might previously have
coded things differently. This standardization has led to an
unexpected bonus: it generates higher-quality structured data
that can be used for machine learning and predictive models,
creating a virtuous cycle of improvement in patient care.

Insurance Integration Agent

In what we see as a brilliant example of a practical AI application,
the company is developing an agent that seamlessly integrates
with pet insurance policies during veterinary consultations.
This agent will automatically check policy coverage while
the veterinarian discusses treatment options with pet owners,
addressing a common pain point where neither vets nor pet
owners are always clear about what treatments are covered. This
real-time information helps both practitioners and pet owners
make more informed decisions about care options.

Store Colleague Assistant

One of the most inspiring implementations is their personalized AI
assistant for retail staff that adapts to specific roles and expertise.
Imagine being a store colleague specializing in aquariums and
having an AI assistant that knows your specialty and proactively
provides relevant operating procedures and product information.
This system not only improves training efficiency but enables
staff to provide exceptionally informed customer service.

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Lessons Learned and Best Practices

Through our analysis of Pets at Home’s journey, we’ve
uncovered some fascinating insights that we believe are crucial
for any organization embarking on an AI agent transformation.

The Critical Role of Data Quality

Ellis’s team made a discovery that we find particularly compelling:
success with AI agents depends heavily on the quality of both
structured and unstructured data. They learned through experience
that while human employees could easily handle conflicts between
different documents or policies, AI agents struggled with these
inconsistencies. Ellis shares an enlightening observation: “If you
provide a SharePoint directory via co-pilot to LLM and ask it a
question, it will find the knowledge, but if it’s two contradictory
bits, it’ll kind of go... I’ll take the first one, or I’ll take the second
one, or I’ll take a mix of two.” What makes this insight so powerful
is how it challenges our traditional focus on structured data and
pushes us to think more holistically about knowledge management
across the enterprise.

The Power of Executive Sponsorship

One of the most inspiring aspects of Pets at Home’s transformation
is its exceptionally strong executive support. Their chairman’s
vision of AI as “the next industrial revolution” didn’t just set
the tone—it catalyzed action across the organization. What’s
particularly clever about their approach is how they focused on
smaller initiatives with passionate executive sponsors rather than
creating large committees. This targeted approach allowed them
to move quickly and demonstrate value, building unstoppable
momentum for larger transformations. It’s a strategy we’ve seen
work time and again, but rarely executed with such clarity of
purpose.

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The Evolution of Cost Management

One of the most intriguing challenges they encountered—
and one we’re seeing across the industry—revolves around
managing AI costs effectively. Ellis makes a fascinating point
about how running agentic AI can be more expensive than
standard automation due to higher token usage and processing
requirements. Each agent is essentially bespoke, making
traditional pricing models inadequate. We’re particularly
excited to see how they’re working with vendors to pioneer
new enterprise pricing frameworks that better reflect the unique
nature of AI agent deployments—work that will likely benefit
the entire industry.

Balancing Automation with Human Oversight

Their nuanced approach to balancing automation and human
oversight is a masterclass in thoughtful AI implementation.
In veterinary care, they ensure human oversight of all AI-
generated content, while in retail operations, they allow for more
automation. As Ellis astutely puts it, “Wherever we have a worry
about the accuracy and relevance of the answer, the human needs
to still be in the loop.” This sophisticated understanding of when
to automate and when to augment human capabilities showcases
the kind of wisdom that comes from deep engagement with AI
technology.

The Impact of Low-Code Development

Perhaps one of the most exciting aspects of their transforma-
tion is their experience with Microsoft’s Copilot Studio, which
revealed the game-changing potential of low-code platforms
to accelerate AI agent deployment. However, this very ease of
development created fascinating new challenges around gover-
nance and financial control. Ellis’s observation really resonates
with us: these platforms are “so easy to deploy and use... so easy
to rack up the costs.” Their innovative response was to develop

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governance frameworks that maintain the benefits of democ-

ratized AI agent development while keeping costs and quality
under control—a balancing act that we believe will become in-
creasingly critical as these technologies proliferate.

The Next Wave of Innovation for Pets at Home

As we explore Pets at Home’s groundbreaking transformation,
we can’t help but be energized by their vision of the future.
Ellis’s team isn’t just implementing today’s technology—
they’re actively shaping what enterprise AI will look like in the
years to come. Working directly with Microsoft’s development
teams in Ireland and Seattle, they’re pushing the boundaries of
what’s possible with enterprise AI agents in ways we’ve never
seen before.
What truly excites us about Pets at Home’s forward-looking
initiatives are three transformative developments that Ellis’s
team has identified for the coming year. Let’s explore each one:

Memory Evolution
The team is tackling one of the most challenging aspects of
AI agents—memory capabilities. This isn’t just about storing
information; it’s about creating AI assistants that truly remember
and learn from every interaction. While Ellis acknowledges that
current solutions using tools like Cosmos DB are temporary, his
team is pioneering approaches that will enable more sophisticated
personalized assistance. This advancement is crucial for creating
those deeply personalized AI experiences that feel genuinely
human.

Multi-Agent Autonomy
Perhaps the most groundbreaking aspect of their work is in
multi-agent autonomous interactions. They’re already testing
agent-to-agent communications with no human in the loop, but
doing it in a way that maintains governance and control. As

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Ellis explains, “We’re working with them on their multi-agent

prototypes now... we’re starting to test agent-to-agent with no
human in the loop.” What makes this particularly remarkable
is they’re achieving this in a low-code environment, making it
accessible to a broader range of organizations.

Edge AI and Personal Robotics

The team anticipates a major breakthrough in personal robotics
and human-edge robotics, combined with small language models
running on devices. This combination could fundamentally
change how we interact with AI in physical spaces. It’s an
approach that brings AI closer to the point of interaction while
maintaining enterprise-level security and control.

Transforming Retail Forever

What truly captures our imagination is Ellis’s provocative vision
for retail’s future. He predicts nothing less than a fundamental
shift in how consumers interact with businesses. “You won’t
go to a website anymore,” he states with conviction. Instead,
customers will simply tell their AI assistants what they need:
“Hey Gemini, go and buy me some stuff.”
This isn’t just speculation—they’re already building the
infrastructure to support this vision. Ellis’s team has identified
over 140 use cases for AI agents across their organization,
demonstrating the massive potential for transformation. What’s
particularly fascinating is how they’re approaching this scale-up,
focusing not just on cost reduction but on revenue generation.
As Ellis points out, “If you can focus on opportunities that are
more about improving revenue that’s uncapped... Cost efficiency
is always capped.”

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The Challenge of Scale

One of the most intriguing aspects of their journey is how
they’re tackling the challenges of scaling AI agents across the
enterprise. The team has developed innovative approaches to
handling critical aspects like:

• Data consistency across knowledge bases

• Integration with existing systems
• Cost management in a rapidly evolving pricing landscape
• Governance of democratized AI development

Looking to the Future

Ellis describes the pace of change as following a “double exponential
curve,” with developments happening at an unprecedented rate.
“Every 3 months every 6 months, it’s doubling. It’s Moore’s law
on steroids,” he shares with infectious enthusiasm. This rapid
evolution means organizations need to be prepared for continuous
transformation.
What makes Pets at Home’s story so compelling isn’t just
their technical achievements—though those are remarkable. It’s
their ability to maintain a human-centric focus while pushing
the boundaries of what’s possible with AI. They’re showing us a
future where technology doesn’t replace human interaction but
enhances it in ways we’re only beginning to imagine.
Ellis perfectly captures the excitement of this technological
moment: “What an exciting time to live and be alive and work
in technology.” Looking at what Pets at Home has achieved, we
couldn’t agree more. Their journey from traditional retailer to AI
pioneer offers invaluable lessons for any organization looking
to thrive in this AI-driven future. They’ve shown us that with
the right vision, leadership, and approach to implementation, the
future of enterprise AI isn’t just promising—it’s already here.

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Agentic Use Cases Across Functions and

Industries
The Pets at Home case study powerfully illustrates how an
organization can successfully implement agentic AI across
its operations. While their journey is unique, it demonstrates
universal principles that apply across industries and functions.
Like Pets at Home, every organization needs to start by
identifying the right use cases that align with their strategic
objectives and operational realities.
As we discussed in Chapter 8, identifying and prioritizing the
right use cases is crucial for successful agentic AI implementation.
While Pets at Home identified over 140 use cases specific to
their business, we have collected a comprehensive set of proven
use cases across industries and functions to help you jumpstart
your own transformation journey. These are detailed in the
appendices, organized into two main categories:
Enterprise AI Agent Applications (Appendix A) covers
fifteen proven implementations across six key areas:

• Operations & Supply Chain, including manufacturing

coordination and supplier communications
• Sales & Revenue Management, showcasing complex
B2B sales orchestration
• Customer Experience & Service, featuring healthcare
access navigation and banking service coordination
• Risk, Compliance & Security, including financial fraud
detection and regulatory documentation
• Knowledge Work & Analytics, covering competitive
intelligence and market research
• Employee & Administrative Services, demonstrating
HR operations and IT service management

Personal Productivity Applications (Appendix B) presents

five fundamental implementations that often serve as excellent

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starting points for organizations beginning their agentic AI

journey, including email management, calendar optimization,
and research synthesis.
We encourage you to explore these use cases in detail in the
appendices, using them as inspiration and practical blueprints
for your own implementation. Like Pets at Home, you may
find that starting with focused, high-impact use cases builds
momentum for broader transformation. Remember, successful
implementation isn’t about deploying as many use cases as
possible, but rather about identifying the ones that will create the
most value for your organization while building your capabilities
for larger-scale transformation.

***

The case studies and use cases presented in this chapter

demonstrate that AI agent implementation isn’t just about
technology—it’s about reimagining how work gets done and
how value is created. From Pets at Home’s ambitious vision
of AI-driven customer interactions to the numerous industry-
specific applications we’ve explored, we see how AI agents are
already transforming businesses in tangible, measurable ways.
Yet these implementations also raise profound questions
about the future of work and society. As we move into Part 5,
we’ll explore these broader implications in detail. How will
increasingly sophisticated AI agents reshape the nature of work
itself? What new opportunities and challenges will emerge as
these technologies evolve? What does this mean for human
workers, organizations, and society as a whole? These questions
aren’t just theoretical—they’re crucial considerations for any
organization embarking on an AI agent transformation journey.

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 PART 5
FUTURE HORIZONS
FOR WORK AND
SOCIETY
 Agentic Artificial Intelligence

A
s we’ve journeyed together through the practical
realities of implementing and scaling AI agents, a larger
question has been taking shape: What happens when
this technology becomes ubiquitous? Having explored how to
build AI agents (Part 3) and transform organizations with them
(Part 4), it’s time to lift our gaze to the horizon and consider the
profound implications for how we work, learn, and live.
This isn’t idle speculation. Throughout our experience
implementing agentic AI, we’ve witnessed firsthand how these
technologies reshape not just processes but people’s lives—
sometimes in ways nobody anticipated. What began as automation
projects often evolved into a fundamental reimagining of work
itself. The changes we’re discussing aren’t decades away; they’re
already emerging in organizations around the world.
Our goal in Part 5 isn’t to predict the future with certainty—
no one can do that. Instead, we aim to help you think
systematically about the choices before us, both individually
and collectively. The future of work and society in the age of AI
agents isn’t predetermined; it will be shaped by the decisions
we make today about how to develop, implement, and govern
these technologies.

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CHAPTER 13
THE NEW WORLD
OF WORK

Work Reimagined: The Symphony

of Human and Machine

I
n our years of consulting and research, we’ve witnessed
countless strategy meetings, but what we observed at a global
real estate company in early 2025 was truly remarkable.
Tara, a senior project manager, wasn’t just sharing updates with
her team—she was orchestrating a sophisticated collaboration
between human creativity and artificial intelligence. While an
AI agent analyzed project data and identified risks in real-time,
Tara’s uniquely human abilities—what we call “Humics”—
enabled her to interpret these insights through the lens of team
dynamics, client relationships, and broader business impact.
“What’s fascinating,” Tara reflected, “isn’t that AI can
handle complex analysis—it’s how developing our distinctly
human capabilities has allowed us to create something greater
than either humans or AI could achieve alone.”

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This observation deeply resonated with us. Through our

work, we’ve come to a profound realization: the future belongs
not to AI alone but to this powerful symphony of human and
machine capabilities.

The Evolution of Human–Agent Collaboration

From our experience, we’ve observed a fascinating progression
in human–agent collaboration. What’s most striking to us is how
this evolution has unfolded across distinct levels of sophistication.
At Level 1, we saw basic rule-based automation—the kind that
could handle repetitive tasks but required explicit programming.
Level 2 brought intelligent automation, where AI could handle
more complex scenarios using machine learning but still within
confined parameters.
The real transformation began with Level 3 agentic
workflows. These AI systems could understand context, reason
with sophistication, and orchestrate complex processes. This
is where we saw the first genuine examples of human–agent
collaboration, where AI wasn’t just a tool but a partner in
problem-solving.
One of our most compelling experiences came from a
manufacturing company we worked with—a transformation
that still excites us when we share it. Their journey perfectly
illustrates what we believe is the natural evolution of human-agent
collaboration. They started with basic robotic process automation
for inventory management (Level 1), progressed to AI-powered
demand forecasting (Level 2), and finally implemented a Level
3 agentic system that could autonomously manage their entire
supply chain, adapting to disruptions and optimizing operations
in real-time. The human workforce didn’t disappear—it evolved
to focus on strategic decisions and oversight while the AI handled
operational complexities.
Consider how work is being reimagined across different
sectors:

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 The New World Of Work

• In healthcare, where we’ve had the privilege of working

with several leading institutions, AI agents handle
routine diagnostics and administrative tasks, freeing
doctors and nurses to focus on complex cases and patient
relationships. What’s particularly inspiring is how the
human touch becomes more valuable, not less, as AI
handles the routine aspects of medical care. Especially
in healthcare, the dominant mindset is clear: AI is in
service of humans. AI agents, if led well, create the time
and space for the interpersonal human touch to become a
bigger part of treating patients. And the new mix of work
is far more satisfying to clinicians.

• In financial services, we’ve observed a fascinating shift:

AI manages data analysis and risk assessment, while
human advisors focus on understanding clients’ life goals
and providing emotional support during major financial
decisions. The results have exceeded our expectations—
the combination delivers better outcomes than either
could achieve independently.

• One of our favorite transformations has been in creative

industries, where AI handles technical aspects of
production—like color correction, sound enhancement,
and scene continuity tracking—while humans focus on
higher-level creative direction and emotional storytelling.
While it’s still the early days of this transition, we’re
already amazed by how this partnership is creating new
forms of artistic expression that weren’t possible before.

New Roles at Each Level

Throughout our consulting work, we’ve had a front-row seat to
what we believe is one of the most exciting developments in
workplace evolution: As AI capabilities progress through these

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levels, new roles emerge while others transform. At Level 1, we

saw the rise of automation specialists and process analysts. Level
2 brought demands for AI trainers and data quality managers.
But what truly excites us is how Level 3 is creating entirely new
categories of work.
What fascinates us most is the emergence of AI Orchestra-
tors as crucial intermediaries between human teams and AI sys-
tems. They understand both human needs and AI capabilities,
ensuring effective collaboration between the two. One of our
most successful cases was at a financial services firm we ad-
vised, where orchestrators helped transform how teams worked
with AI, moving from simple task automation to complex deci-
sion support systems that enhanced human judgment rather than
replacing it.
Of course, as AI systems gain more autonomy, questions
about decision-making transparency and ethical boundaries
need careful consideration. For this reason, we’ve become
particularly passionate about the role of Ethics Officers and AI
Auditors. In our experience, they play a crucial role in ensuring AI
decisions align with human values and organizational principles.
We’ll never forget working with a healthcare provider that
created an entire department dedicated to monitoring their AI
systems’ decision-making in patient care, ensuring both ethical
compliance and more accurate recommendations and decisions,
which provided a reliable and responsible work environment for
human doctors to focus more on the interpersonal side of the
treatment.

The Three Competencies of the Future

Through our work, we’ve identified what we believe are the
three essential competencies needed for success in the agentic
age. As detailed in the book “IRREPLACEABLE: The Art of
Standing Out in the Age of Artificial Intelligence,” these are

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Change-Ready, AI-Ready, and Human-Ready capabilities.194

What’s crucial to understand is that these aren’t just skills—
they’re mindsets and approaches that enable people to thrive
alongside AI.
Being Change-Ready means developing resilience and
adaptability in the face of continuous transformation. It’s about
viewing change not as a threat but as an opportunity for growth.
One executive we worked with described it as “developing the
muscle memory for adaptation”—just as athletes train their
bodies to respond instinctively, workers need to build their
capacity to embrace and navigate change. This has always been
a useful attribute for employees, but it’s even more critical in the
age of AI and agents.
AI-Ready competency involves understanding how to
work effectively with AI systems. This goes beyond technical
knowledge—it’s about developing an intuitive sense of AI’s
capabilities and limitations. Workers need to learn when to
rely on AI and when to apply human judgment. A legal firm we
advised saw dramatic improvements in case outcomes when their
lawyers learned to combine AI’s data analysis capabilities with
their own strategic thinking and emotional intelligence. The 2016
book Only Humans Need Apply described this competency as
“stepping in”—understanding how AI systems work, observing
their performance, and making them better as required.195
The Human-Ready competency focuses on developing
uniquely human capabilities that AI cannot authentically
replicate—what we call the “Humics.” These include genuine
creativity (which are novel ideas that are useful and meaningful
to humans and society and thus not simply the recombination of
existing ideas), critical thinking (including ethical judgment and

194
Pascal Bornet, 2024. “IRREPLACEABLE: The Art of Standing Out in
the Age of Artificial Intelligence,” https://irreplaceable.ai/
195
Thomas H. Davenport and Julia Kirby, 2016. “Only Humans Need Apply:
Winners and Losers in the Age of Smart Machines,” https://www.amazon.
com/Only-Humans-Need-Apply-Machines/dp/0062438611

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intuitive understanding), and social authenticity (the ability to

build genuine human connections based on shared values).

The Humics Advantage

What we find most compelling about the Humics approach—and
we’ve seen this consistently across industries—is that, unlike
technical skills that may become obsolete, these fundamental
human abilities are timeless and serve as fertile soil from which
new, relevant skills naturally emerge:
Time after time, we’ve seen how Genuine Creativity goes
beyond AI’s ability to recombine existing ideas. We’ve watched
in amazement as professionals conceive truly original concepts
driven by human emotional depth and lived experience. A perfect
example comes from a digital marketing agency we advised,
where teams focused on enhancing their creative capabilities
started developing entirely new approaches to storytelling that
combined data insights with emotional resonance in ways AI
alone couldn’t achieve.
Through our work, we’ve come to understand that Critical
Thinking encompasses our ability to make nuanced judgments,
question assumptions, and navigate ethical complexities.
Sharpening critical thinking includes the art of being curious
and asking questions that enable you to deal with uncertainties
and complex situations more effectively.
A particularly inspiring example comes from a financial
services firm we worked with, where advisors sharpened their
critical thinking through workshops and case study discussions.
As a result, they naturally developed new capabilities in ethical
investment strategy and holistic client assessment—skills that
didn’t compete with AI’s analytical power but complemented it,
allowing for more well-rounded decision-making.
Another rewarding insight has been witnessing the power
of social authenticity in fostering genuine human connection,
empathy, and trust. We saw this in action at a healthcare provider,

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where practitioners focused on enhancing their social authenticity

through patient communication training, role-playing exercises, and
mentorship programs. This emphasis on human connection helped
them develop stronger communication skills and a more holistic
approach to patient care, working in harmony with AI diagnostic
tools rather than being replaced by them.

From Humics to New Skills

What truly excites us about the Humics approach is how it
enables organic skill development that is aligned with emerging
needs. Let us share some of our favorite examples:
Jordan, a remarkable marketing professional we had the
pleasure of working with, focused on developing his genuine
creativity and critical thinking. We were amazed to watch
how this foundational capability led to the natural emergence
of new skills like innovative campaign design and data-driven
creativity. Rather than trying to predict which specific marketing
skills would be valuable in the future, Jordan’s investment in
Humics allowed him to adapt and innovate naturally as the field
evolved.
Elena, whose transformation particularly inspired us, provides
another compelling example. When AI agents began handling
portfolio analysis and market predictions at her global bank, Elena
doubled down on developing her Humics, particularly her social
authenticity and critical thinking. What happened next exceeded
even our expectations: her enhanced empathy enabled her to deeply
understand clients’ emotional relationships with money and their
broader life goals. Her critical thinking allowed her to synthesize
AI-generated market insights with clients’ personal circumstances
and values. As a result, she developed new skills in “holistic finan-
cial life planning” and “AI-enhanced emotional intelligence in fi-
nance”—capabilities that emerged organically from her strength-
ened Humics foundation.

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A related example we observed was at an asset management

company that was beginning to implement a set of AI agents for
wealth management—what is popularly called “robo-advice.”
In an interview about the new system, a financial advisor told us,
“I’m hearing the footsteps behind me on this robo-advisor thing.
Rather than trying to compete with it, I’m trying to learn more
about ‘financial psychiatry’—for example, trying to reconcile
the often widely varying perspectives of husbands and wives in
married couple clients.”
What’s particularly noteworthy in both cases is how the
development of new skills wasn’t forced or predetermined. Just
as healthy soil naturally supports the growth of various plants,
strong awareness of and focus on Humics create the conditions for
relevant skills to emerge in response to changing circumstances.
Jordan’s and Elena’s experiences demonstrate how investing in
these fundamental human capabilities enables professionals to
remain relevant and valuable as AI capabilities expand.

Emerging Skill Categories

We are currently observing the emergence of new skill categories
rooted in strong Humics foundations. Here are a few examples:
Emotional Innovation combines genuine creativity with
social authenticity to craft deeply resonant human experiences.
This skill is becoming increasingly evident in fields such as
product design and urban planning, where professionals must
develop solutions that address both practical and emotional
human needs.
Intuitive Systems Navigation leverages critical thinking
alongside social authenticity to manage complex systems involving
both human and AI elements. This skill is particularly essential in
industries like healthcare and supply chain management, where
understanding the interplay between technological capabilities and
human factors is critical for success.

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 The New World Of Work

Finally, Complex Ethical Decision-Making draws on all

three Humics dimensions to address the increasingly intricate
ethical challenges of an AI-driven world. This skill is especially
valuable in areas such as AI development, healthcare, and
financial services, where balancing technical capabilities with
human values and societal impact is paramount.

Figure 13.1: The Agentic AI Progression Framework (Source: © Bornet et al.)

Supporting Humics Development

We firmly believe that organizations need to actively support the
development of these foundational capabilities. We’ve found the
following approaches to be particularly effective:

• One of our most successful strategies has been creating

learning environments that encourage experimentation
and reflection, allowing employees to develop their
Humics through real-world challenges and experiences.

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 Agentic Artificial Intelligence

• We’ve seen remarkable results from implementing

measurement systems that recognize and reward the
development of Humics-based skills, moving beyond
traditional performance metrics to value human
capabilities that complement AI.

• What’s proven particularly powerful in our experience is

designing work processes that optimize the collaboration
between human Humics and AI capabilities, ensuring
each contributes their unique strengths to achieve better
outcomes.

We’ve discovered that by focusing on developing Humics,

organizations can build workforces that remain relevant and
valuable as AI capabilities continue to evolve. One of our most
important learnings has been that specific and new skills will
emerge naturally when we nurture those fundamental human
capabilities first.
As we move deeper into the agentic age, we believe the most
successful organizations will be those that create environments
where human capabilities can flourish alongside AI. By focusing
on developing the Humics, we create the conditions for continuous
adaptation and innovation, ensuring that human workers remain
irreplaceable even as AI capabilities expand.
The future of work isn’t about humans versus AI—it’s about
creating a symphony where both play to their strengths. We
believe passionately that by focusing on developing our uniquely
human capabilities, we create the foundation for continuous
adaptation and innovation in the agentic age. The organizations
that thrive will be those that understand this fundamental truth:
our human capabilities, when properly developed, will allow
humans to employ AI in ways that will create possibilities that
one could not achieve alone and, as such, succeed in securing
value at unprecedented levels.

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This Time Is Different: The Dawn

of Agentic AI
“I’ve automated myself out of a job,” Debbie confessed during
our consulting engagement at a Fortune 500 company. As a
veteran project manager, she had just implemented a Level 3
AI agent that not only coordinated an entire software release
but learned from its mistakes and adapted its approach—much
like she would. “In twenty years of managing tech projects, I’ve
seen countless tools come and go. But this isn’t a tool. This thing
thinks like me.”
That moment will stay with us forever. Debbie’s reaction
resonated deeply with us. Her experience crystallizes why
the agentic AI revolution fundamentally differs from previous
technological transformations. We’re not just observers—we’re
witnesses to something unprecedented in both velocity and
nature of change.

The Acceleration Paradox

Throughout our decades of implementing automation technolo-
gies across industries, we’ve had front-row seats to many waves
of technological change. But what we’re seeing now with agen-
tic AI fills us with both excitement and concern—it’s unlike any-
thing in our experience.
Looking back through the lens of history, technological rev-
olutions followed a predictable pattern: disruption, adaptation,
and eventual equilibrium. The Industrial Revolution’s automa-
tion of physical labor unfolded over generations. The Digital
Revolution’s computerization of cognitive tasks spanned de-
cades. Each wave gave society time to adapt its educational sys-
tems, labor markets, and social structures.
But what fascinates and, frankly, sometimes alarms us is
how agentic AI shatters this pattern. Let us share one of our
most striking experiences: We recently implemented a Level 3

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agent to manage supply chain for a manufacturing client. What

happened next amazed even us—within 47 days, it was handling
complex logistics decisions that previously required their most
experienced professionals. Even more remarkable was how it
evolved—teaching itself from each interaction, refining its
decision-making, and eventually surpassing human performance
in areas requiring context understanding.
This rapid transformation led us to identify what we consider
the “adaptation paradox”: just as the skills needed to work
alongside AI become more sophisticated, the time available
to develop these skills dramatically shrinks. We witnessed
this firsthand at a financial services firm we advised, where
employees had less than six months to transition from processing
transactions to orchestrating complex AI workflows—a shift
that traditionally would have taken years of gradual upskilling.
We also witnessed this challenge at a tech company we
advised, where their AI development team’s skills were
becoming obsolete every three months as their agents evolved.
What particularly struck us was how traditional quarterly
training cycles proved futile—by the time employees mastered
new skills, the technology had advanced further. This experience
led to what we consider a breakthrough realization: the need for
a radical rethinking of professional development, shifting from
periodic training to continuous, AI-assisted learning.

The Nature of Disruption

Through our extensive work with AI implementations, we’ve
come to understand that the unique character of this disruption
becomes clear when we examine the Agentic AI Progression
Framework. Level 1 and 2 agents followed the historical pattern
of automation—replacing routine physical and cognitive tasks.
However, what we find truly revolutionary about Level 3 agents
is how fundamentally different they are. They don’t just execute
tasks; they understand context, learn from experience, make

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nuanced decisions, and provide steering recommendations.

This reality makes clear that AI agents are becoming almost
self-reflective as they can analyze their own decision-making
processes and refine their algorithms autonomously.
One of our most eye-opening experiences came from a
legal firm we worked with that implemented an agent to review
contracts—traditionally a task requiring years of specialized
training. What surprised us most wasn’t just that within months,
the agent wasn’t just processing documents faster; it was
identifying subtle legal risks that even senior lawyers occasionally
missed. But what truly amazed us was watching it learn from
each contract, continuously improving its understanding of legal
nuance and business context.
This transformation has led us to one of our most important
realizations: traditional adaptation strategies may fail. Previous
technological revolutions allowed humans to move “up the value
chain” to more complex cognitive work. But we’re now facing an
unprecedented challenge: when AI can learn, reason, and adapt,
what’s the higher ground to which humans can move? Through
our work with numerous organizations, we’ve discovered that
the answer lies not in competing with AI but in fundamentally
reimagining human-AI collaboration.

Learning from History While Breaking

New Ground
While we acknowledge that this transformation is unprecedented,
our research and experience have shown us that history offers
crucial lessons. We’ve identified that successful transitions
in previous revolutions shared common elements: proactive
adaptation, focus on uniquely human capabilities, and strong
institutional support. But what makes our current situation
particularly challenging is that these lessons must be applied at
an unprecedented pace and scale.

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Through our work with forward-thinking organizations,

we’ve observed that companies successfully navigating this
transition treat AI agents not as tools but as collaborators
requiring new management approaches. What’s particularly
encouraging is how they focus on developing their workforce’s
uniquely human capabilities—creativity, emotional intelligence,
and complex problem-solving—while leveraging AI’s analytical
and learning capabilities.

The Path Forward

As we stand at this inflection point, we’re more convinced than
ever that agentic AI represents not just another wave of automation
but a fundamental shift in human-machine collaboration. What
keeps us up at night isn’t whether this change will happen—it’s
already unfolding. What truly matters, in our view, is ensuring
this transformation benefits organizations, individuals, and
society.
In the chapters ahead, we’ll share what we believe are
concrete strategies for thriving in this new reality. While we
acknowledge that the window for adaptation may be shorter
than ever, we remain optimistic because we’ve seen firsthand
how organizations and individuals who understand and embrace
this change can unlock extraordinary opportunities. Our goal is
to help individuals not just survive this transformation but to
thrive in it.

Reinventing Education in the Age of

AI Agents
As agentic AI fundamentally reshapes our world at unprecedent-
ed speed, we must recognize that traditional adaptation strategies
will no longer suffice. Because this time is truly different, we
need to reconsider the very foundation of human skills and ca-
pabilities. If we are to navigate this transformation successfully,

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we must return to first principles and redefine what it means to

be educated in a world where humans and increasingly sophisti-
cated AI agents collaborate as partners rather than competitors.

The Crisis in Modern Education

The rise of AI agents is fundamentally transforming how we
live and work, yet our education systems have remained largely
unchanged since the Industrial Revolution. Consider a typical
classroom today: students still memorize facts they could easily
retrieve from an AI agent, practice skills that will soon be
automated, and follow standardized curricula that fail to nurture
the uniquely human abilities that will matter most in the future.
Instead of inculcating critical thinking, students are discouraged
from questioning what they are taught. This industrialized
education approach made sense in an era when information was
scarce and routine cognitive tasks were performed by humans.
But in a world where AI agents can instantly access and process
vast amounts of information, our educational priorities must
shift dramatically.

Returning to Education’s True Purpose

One of our most fascinating discoveries in researching this
topic was that the word “school” comes from the Greek word
“skholē,” meaning “leisure” or “rest.” We find this etymology
deeply revealing because it uncovers a profound truth about
education’s original purpose: it wasn’t about preparing workers
for jobs, but about providing space for thoughtful reflection and
exploration of life’s fundamental questions. What inspires us
about the ancient Greeks’ approach is how they saw education
as a means to help people find their purpose and develop their
full potential as human beings. That was, of course, the original
idea behind liberal arts education in colleges as well, but the idea
has lost popularity over time.

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In our view, this historical perspective offers a powerful

critique of today’s career-oriented approach, where success is
often measured by test scores and job placement rates. Instead,
we believe that education needs to equip individuals with the
drive to develop, grow, and promote reflective thinking that looks
for opportunities to accelerate these developmental processes.
Humanity is best served by understanding what purpose
underlies human development, as it will provide meaning to our
actions and create awareness about the opportunities (such as AI
today) around us to benefit from. As AI agents can increasingly
handle routine cognitive tasks and help facilitate opportunity
creation, we believe it’s time to return to this more holistic view
of education—one that emphasizes human development over
job training. From this perspective, philosophy, psychology,
and behavioral approaches to technology should become more
central to our overall education approach.

Reimagining Education Through the Three

Competencies
Having explored the Three Competencies of the Future earlier
in this book, let’s examine how they can reshape our approach
to education.196 These competencies aren’t just skills to be
taught—they should be the organizing principles around which
we structure all learning experiences.
In practice, this means transforming traditional subjects
through the lens of these competencies. Mathematics, for
instance, should shift from memorization of formulas (which
AI can handle) to developing problem-solving strategies and
creative approaches to mathematical challenges so that math
comes alive by exploring its impact. One of our favorite examples
is how literature classes can focus less on plot recall and more

196
Pascal Bornet, 2024. “IRREPLACEABLE: The Art of Standing Out in
the Age of Artificial Intelligence,” https://irreplaceable.ai/

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on deep analysis, emotional understanding, and the exploration

of human experiences that AI agents cannot truly comprehend.

Global Innovation in Education

Throughout our research and experience, we’ve witnessed some
truly inspiring examples of countries pioneering educational
reforms that align with these principles. We’ve been particularly
impressed by Finland’s transformation of its curriculum to
emphasize interdisciplinary learning and real-world problem-
solving. What fascinates us about their approach, called
“phenomenon-based learning,” is how it breaks down traditional
subject boundaries. We’ve seen firsthand how, when studying
climate change, students simultaneously engage with scientific
data (developing AI-Ready analytical skills), explore creative
solutions (strengthening Human-Ready capabilities), and adapt
their understanding as new information emerges (building
Change-Ready competencies).197
Another model that we find particularly compelling is
Denmark’s education system with their “Klassens Tid” (Class
Time) program. Since 1993, students aged 6-16 spend one
hour each week developing emotional intelligence and social
authenticity. What we love about these sessions is how students
learn to navigate complex social situations, resolve conflicts,
and build genuine human connections—skills that we believe
become increasingly valuable as AI agents handle more of our
routine interactions.198
One of the most forward-thinking approaches we’ve studied
is Singapore’s “SkillsFuture” initiative, which demonstrates
how education systems can evolve to support lifelong learning.

197
Pasi Sahlberg, 2021. “Finnish Lessons 3.0: What Can the World Learn
from Educational Change in Finland?”
198
Alessia Lalomia and Antonia Cascales-Martínez, 2023. “Social-emotional
Skills Development: The Design of a Project in a Danish School,” https://doi.
org/10.18662/rrem/15.2/726

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We’re particularly excited about their innovative approaches,

including regular skills forecasting to anticipate future needs,
flexible learning pathways that allow students to combine
traditional academics with practical skills, integration of AI tools
into the learning process while maintaining focus on human
development, and a strong emphasis on project-based learning
that develops adaptability and creativity.199
These new approaches to education don’t apply only to
elementary and secondary school students. Joseph Aoun, the
president of Northeastern University, published a book in 2017
entitled Robot Proof that defined a new approach to university
education in the age of AI.200 He argued that stuffing college
students’ minds with facts was no longer appropriate, given the
capabilities of AI. Instead, he believes colleges should cultivate
a creative mindset and the ability to create something valuable
to society, whether it’s an artistic work or a new treatment for a
disease.

The Classroom of Tomorrow

We believe we should envision future classrooms as spaces
where students learn to leverage AI while strengthening their
uniquely human capabilities. Instead of competing with AI at
tasks it does better, students should focus on developing comple-
mentary abilities that enhance human-AI collaboration. Some of
those abilities will involve understanding how computers and AI
think, but the majority are more human-focused.
We’ve seen this transformation successfully manifest in
various ways across subjects. One of our favorite examples
is in writing classes, where students use AI agents for basic
drafting and editing but focus their energy on developing unique

199
SkillsFuture Singapore Agency, 2024. “Annual Report 2024: Levelling-
up the Skills Ecosystem”
200
Joseph E. Aoun, 2017. “Robot-Proof: Higher Education in the Age of
Artificial Intelligence”

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voices and compelling narratives. This is a major pivot for

many students, who have been encouraged to just complete a
writing assignment and move on. Now, they need to become
creative writers and critical editors. We’ve also seen science
projects in which AI handles data analysis while students focus
on forming creative hypotheses and interpreting results in novel
ways. And we’ve observed history lessons that leverage AI
to provide factual information, freeing students to engage in
deeper discussions about historical patterns, human motivation,
and ethical implications.

The Teacher’s Evolving Role

In this new educational paradigm, teachers become less informa-
tion providers and more learning architects. Their role shifts toward
designing experiences that develop students’ uniquely human capa-
bilities, guiding students in effective collaboration with AI agents,
facilitating meaningful discussions and deeper understanding, and
helping students recognize and strengthen their distinctly human
contributions. For example, teachers become coaches, facilitating a
transition from content (AI generates content and provides recom-
mendations) to knowledge (humans applying content to make sense
of their own business reality).

The Path Forward

Perhaps needless to say, this transformation requires significant
changes in how we structure and deliver education. We strongly
believe we must redesign assessment methods to evaluate
human capabilities rather than memorized knowledge, invest
in teacher training focused on AI integration and human skill
development, create flexible learning environments that support
both individual exploration and collaborative learning, and
develop new curricula that emphasize human development
alongside technical competency. Educational leaders—from
university presidents to elementary school principals—will

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need to become AI-savvy change managers, just as corporate

executives need to adopt such roles.
What keeps us up at night is the realization that the stakes
couldn’t be higher. The successful functioning of our societies
and our economies depends upon this educational transformation.
As AI agents become more sophisticated, we’re seeing firsthand
how the gap between those who can adapt and those who cannot
continues to widen. But we’re also filled with hope because by
reinventing education around these principles, we can ensure
that future generations are not just prepared to survive in a world
of AI agents, but to thrive in it, remaining irreplaceable in their
own unique ways.
The measure of educational success in this new era won’t be
how much information students can retain or how well they can
perform standardized tasks. Instead, success will be measured
by how effectively students can apply their uniquely human
capabilities in collaboration with AI agents to solve complex
problems and create new value. This is the true challenge—and
opportunity—of education in the age of AI agents.

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CHAPTER 14
SOCIETY IN THE
AGE OF AGENTS

Reimagining Human Potential in an

Agent-Powered World

T
he first question people ask when confronting the rise of
AI agents is often about jobs—will they take mine? But
perhaps we’re asking the wrong question. What excites
us most is a different possibility: what if, instead of threatening
our livelihoods, the emergence of autonomous agents offers
us something revolutionary: the opportunity to fundamentally
reimagine what it means to be human in the modern world?

The End of Work as We Know It?

Let’s begin with what we consider an uncomfortable but
necessary truth: much of today’s work is deeply unfulfilling.
We’ve seen this firsthand in organization after organization,
and the data confirms our observations. According to extensive
Gallup research, a staggering 77% of employees worldwide

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report feeling disengaged from their work.201 What breaks our

hearts is hearing how they describe their daily tasks as repetitive,
tedious, and ultimately unrewarding. One particularly striking
example we’ve encountered is that in the United States alone,
over 1.5 million people spend their days performing repetitive
bookkeeping and accounting tasks that drain rather than fulfill.202
But what truly alarms us is that work isn’t just unfulfilling—
it’s literally killing us. The statistics we’ve uncovered are
shocking: The International Labour Organization reports that
work-related stress and resulting illnesses lead to nearly 3
million deaths annually,203 with a societal cost of approximately
$3 trillion.204 To help grasp the magnitude of this crisis, consider
that twice as many people die from work-related causes as
from road accidents,205 and ten times more than from war.206
We’ve become convinced that the current system simply isn’t
sustainable.
Through our work implementing AI solutions across indus-
tries, we’ve witnessed how AI agents progress through increasing

201
Gallup, 2024. “State of the Global Workplace: 2024 Report,” https://
www.gallup.com/workplace/349484/state-of-the-global-workplace.aspx
202
U.S. Bureau of Labor Statistics, 2024. “Bookkeeping, Accounting, and
Auditing Clerks,” Occupational Outlook Handbook. https://www.bls.gov/
ooh/Office-and-Administrative-Support/Bookkeeping-accounting-and-
auditing-clerks.htm
203
ILO (International Labour Organization), 2023. “Nearly 3 million people
die of work-related accidents and diseases,” https://www.ilo.org/resource/
news/nearly-3-million-people-die-work-related-accidents-and-diseases
204
Dietmar Elsler, Jukka Takala, and Jouko Remes, 2019. “An International
Comparison of the Cost of Work-Related Accidents and Illnesses,” European
Agency for Safety and Health at Work (EU-OSHA). https://osha.europa.
eu/sites/default/files/2021-11/international_comparison-of_costs_work_
related_accidents.pdf
205
World Health Organization (WHO), 2023. “Road Traffic Injuries,” https://
www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
206
Ben Knight, 2023. “Global Conflicts: Death Toll at Highest in 21st
Century,” https://www.dw.com/en/global-conflicts-death-toll-at-highest-in-
21st-century/a-66047287

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levels of capability—from basic rule-based automation to more

sophisticated autonomous operations. As they mature in techno-
logical sophistication and capability, they offer us a chance to re-
shape this reality. While our experience shows that current Level
1-3 agents are primarily augmenting human capabilities, we’re
excited by how the progression toward Level 4 and, eventually,
Level 5 agents points to a future where machines could handle a
significant portion of traditional economic activity.

A Historical Perspective on Liberation

One of our favorite historical insights comes from 1930, when
economist John Maynard Keynes wrote an essay titled “Economic
Possibilities for Our Grandchildren.”207 What fascinates us
about his vision is how he envisioned a future with dramatically
reduced working hours. He predicted that by the 21st century,
technological advancement would enable 15-hour workweeks—
less than two full days by today’s standards. What’s particularly
striking to us is that his vision wasn’t about unemployment but
about liberation from back-breaking and mind-numbing work.
In our conversations with industry leaders, we’ve heard
similar predictions. Tech leaders like Jack Ma have suggested that
AI could reduce the workweek to just three days, with four-hour
workdays.208 Bill Gates recently made a similar prediction.209
While this might sound utopian, we’ve been particularly
inspired by early experiments with reduced work schedules

207
John Maynard Keynes, 1930. “Economic Possibilities for Our Grandchildre,”
https://www.aspeninstitute.org/wp-content/uploads/files/content/upload/Intro_
Session1.pdf
208
Aimee Picchi, 2019. “Billionaire Jack Ma, booster of 12-hour days,
now says AI will allow 12-hour weeks,” https://www.cbsnews.com/news/
billionaire-jack-ma-booster-of-12-hour-days-now-says-ai-will-allow-12-
hour-weeks/
209
Aislinn Murphy, 2023. “Bill Gates says using AI could lead to 3-day
work week,” https://www.foxbusiness.com/technology/bill-gates-suggests-
artificial-intelligence-could-potentially-bring-three-day-work-week

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yielding promising results. One of our favorite examples is

New Zealand’s Perpetual Guardian, an estate planning company
that trialed a 32-hour workweek for its 240 employees without
reducing salaries. The results amazed even us: stress levels
decreased, engagement improved, and productivity surged by
30-40%.210
In the UK, 61 companies participated in a four-day workweek
experiment with 100% of previous compensation. The results
were uniformly positive. Employees had better sleep, less stress,
better mental health, and more fulfilling personal lives. Company
revenues stayed the same or grew compared to previous periods.
56 of the 61 companies decided to continue with the work
arrangement after the pilot ended.211
These new work experiments were conducted before the
widespread advent of AI agents and even generative AI. Imagine
how much human labor could be productively automated with
these new tools, and how much more fulfilling employees’ lives
could be if they could work substantially fewer hours, still be
more productive, and make the same amount of money.

Beyond the Paycheck: Reimagining Value

One of the most profound challenges—and opportunities—
presented by AI agents is the chance to redefine how we measure
human value. One deeply troubling aspect of the current system is
the inverse relationship between social value and compensation
in many societies. Consider something we’ve all experienced:
caregiving. This is one of the most essential functions in any

210
Aimee Picchi, 2018. “What happened when a company paid its workers
to work a 4-day week,” https://www.cbsnews.com/news/one-business-says-
a-4-day-week-with-pay-for-5-works/
211
Annabelle Timsit, 2023. “A four-day workweek pilot was so successful
most firms say they won’t go back,” https://www.washingtonpost.com/
wellness/2023/02/21/four-day-work-week-results-uk/

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 Society In The Age Of Agents

society. Whether it’s raising children or caring for the elderly,

this vital work is often poorly compensated or not paid at all.
If caregiving activities could be performed more productively
with the assistance of AI agents and robots, perhaps the humans
who oversee them would finally receive the respect and
compensation they deserve. Those receiving care might also
find it more fulfilling. So far, robots in eldercare have not been
particularly successful, even in societies like Japan that have
invested heavily in them.212 However, it seems possible that AI
agents and smarter robotic systems could significantly enhance
their effectiveness. As with other jobs, human caregivers will
still be essential and need to be valued more highly than they
are today.
We’ve been particularly influenced by the post-workist
movement, led by thinkers like David Graeber213 and Helen
Hester.214 Their compelling argument, which aligns with what
we’ve observed in our work, is that the automation of traditional
work by machines will force us to radically rethink how we
structure society and distribute resources. What excites us most
is that this isn’t just about shorter working hours—it’s about
fundamentally reconsidering what we value and why.

Embracing Purpose in an AI-Driven World

One of the most thought-provoking questions we encounter in
our work is: If we worked less or not at all, how would we spend
our time? Based on our research and conversations with workers

212
James Wright, 2023. “Inside Japan’s experiment in automating eldercare,”
https://www.technologyreview.com/2023/01/09/1065135/japan-automating-
eldercare-robots/
213
David Graeber, 2018. “Bullshit Jobs: A Theory,” https://en.wikipedia.org/
wiki/Bullshit_Jobs
214
Andy Beckett, 2018. “Post-work: The Radical Idea of a World Without
Jobs,” https://www.greeneuropeanjournal.eu/post-work-the-radical-idea-of-
a-world-without-jobs/

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 Agentic Artificial Intelligence

across industries, we believe we might focus on purposeful

pursuits like caring for others and protecting the planet.
Surveys in organizations indeed show that if AI saves
employees time, they would like the organization to allow
them to use that saved time for “meaningful” things.215 If the
adoption of AI agents can create time and liberation for humans,
then one of the most exciting opportunities that may await us
is a reality where humans can focus more on personal growth,
self-development, and stronger community engagement that
can create a world where we can truly pursue the collective
welfare.216
We’ve seen glimpses of this future in our work with forward-
thinking organizations, where people with more free time
explore hobbies, take up entrepreneurial projects, or learn new
skills and languages. What’s particularly inspiring is watching
people travel, volunteer, and strengthen social bonds through
meaningful connections. We’ve observed that when prioritizing
mental and physical health becomes easier, it naturally fosters
well-being and fulfillment.
Drawing from historical parallels that fascinate us, just as
the Industrial Revolution expanded leisure time and spurred
artistic innovation, we believe AI could lead to new forms of
entertainment, learning, and community spaces. In our vision,
offices might transform into hubs for art, play, and collaboration,
which in turn will drive creativity and innovation.
Even though this is an appealing future, a recurring concern
is the fear of an economic collapse when work hours are reduced.
Our research and experience, however, suggest that the right use
of AI agents would likely drive economic growth, giving people

215
Visier, 2023. “New Research on AI’s Impact on Jobs, Time Saved Has
Employees Divided,” https://www.visier.com/blog/ai-impact-on-jobs-
employees-divided/
216
Adecco Group, 2024. “AI Saves Workers an Average of One Hour Each
Day,” https://www.adeccogroup.com/our-group/media/press-releases/ai-saves-
workers-an-average-of-one-hour-each-day

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 Society In The Age Of Agents

more time to enjoy life and engage in the consumer market.

We’re particularly struck by Henry Ford’s observation in 1924
that leisure fuels demand for products, enabling economies to
thrive. The possibilities we see are vast, and the future is full of
potential.

Universal Basic Income: A Foundation

for Human Flourishing?
As AI agents take on more traditional economic roles, we
need new mechanisms to ensure everyone can meet their basic
needs. We’re particularly intrigued by universal basic income
(UBI) as one possible solution—providing every adult citizen
with a guaranteed income sufficient to cover basic necessities,
regardless of employment status.217 Such programs may become
an absolute necessity if technologies like agentic AI lead to
large-scale employment loss, although that is not an outcome
we expect.
What we find most compelling about UBI is that unlike
welfare programs tied to employment status or specific
conditions, it’s universal and non-means-tested, meaning it is
provided to everyone regardless of their income, job status,
or personal circumstances. UBI has been tested in a variety
of settings, although they have been on a small scale thus far.
Results from one of the largest and most recent UBI experiments,
which are consistent with previous studies, suggest that it often
reduces the amount of work that recipients do and lowers their
anxiety about meeting basic needs such as housing.218 It does
not, however, often lead to some of the desired behaviors we

217
Wikipedia contributors, 2025. “Basic Income,” https://simple.wikipedia.
org/wiki/Basic_income
218
Karl Widerquist, 2020. “The Deep and Enduring History of Universal
Basic Income,” https://thereader.mitpress.mit.edu/the-deep-and-enduring-
history-of-universal-basic-income/

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would like to see, such as self-improvement, entrepreneurial

activities, or spending time with family.219
Let us be clear: we have no interest in advocating for UBI if
it leads to a society of idle consumers. We hope that, eventually,
UBI could provide the foundation for people to pursue more
meaningful contributions to society. When freed from the
immediate pressure of survival, perhaps people could invest
time in community service, creative pursuits, environmental
stewardship, or caring for others—all activities that create
tremendous social value but often generate little or no income in
our current system. Accomplishing these objectives, however,
would require the same types of social engineering that we
believe are needed in the workplace.

A More Human Future

After years of implementing AI solutions and witnessing their
impact, we’ve come to a profound realization: The advent
of autonomous agents presents us with a crucial choice. We
can continue trying to compete with machines at tasks they’ll
increasingly dominate, or we can use this technological revolution
as an opportunity to become more fully human and complement
the work of those machines.
An important idea to keep in mind is that as AI agents prog-
ress through higher levels of capability, the pressure to reorga-
nize society will only increase. In fact, we’re convinced that this
transformation will happen, and as such, we’ll need to be clear
on how we’ll shape it. How to do so underscores the question
that is really driving our work: “Will we use this technology to
enhance human flourishing, or will we allow it to exacerbate
existing inequalities and social tensions?”

219
Peter Jacobsen, 2024. “A second working paper shows that people who
receive a guaranteed income tend to work less,” https://fee.org/articles/a-
second-working-paper-shows-that-people-who-receive-a-guaranteed-
income-tend-to-work-less/

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 Society In The Age Of Agents

Based on our experience guiding organizations through

digital transformation, we believe passionately that the time to
start preparing for this future is now. While current production AI
agents may currently be limited to Levels 1-3 of the Progression
Framework, the trajectory is clear, and higher levels of AI agents
will arrive sooner rather than later. But this should not make
us fearful. Instead, we’re optimistic that by making conscious
choices about how we integrate this technology and reorganize
our social structures, we can help create a future that enhances
rather than diminishes what makes us uniquely human.
Perhaps the most powerful insight we’ve gained through our
work comes from Kai-Fu Lee, a leading computer scientist, and
AI expert, who reminds us that the freedom gained through AI
should allow us to “focus on what truly makes us human: loving
and being loved.” This encapsulates what we believe is the real
promise of AI agents—not just to automate our work, but to help
us rediscover and nurture our humanity.

A Framework for Governing the Future of

Agentic AI
In our years of working with artificial intelligence, we’ve never
encountered anything quite as fascinating—or as challenging—
as the rise of agentic AI. As we stand at this pivotal moment in
technological history, we believe society faces unprecedented
opportunities and risks that keep us awake at night. Through our
work with companies across industries, we’ve seen firsthand
how these powerful systems promise extraordinary benefits in
efficiency and innovation. Yet, we’ve also witnessed potential
risks that could fundamentally reshape our world in ways we
may not desire. The key question isn’t whether to develop these
technologies—they’re already here—but how to ensure they
remain under meaningful human control while maximizing their
benefits to society.

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The Unique Challenge of Agentic AI

From our extensive work implementing AI systems, we’ve
discovered something remarkable that sets agentic AI apart:
its extraordinary capacity for autonomous decision-making
and adaptation. This isn’t just another incremental advance in
AI—it’s a fundamental shift that we believe will reshape our
relationship with technology. Unlike today’s AI systems, which
operate within clearly defined parameters and require explicit
human guidance, agentic AI can set its own objectives, learn
from its environment, and make decisions independently. We’ve
found this autonomy introduces entirely new categories of risk
that our current regulatory frameworks and corporate governance
structures simply aren’t equipped to handle.
In our research and practical experience, we’ve observed how
the complexity of control increases exponentially as we progress
through the levels of AI agency—from basic rule-based automation
to fully autonomous systems. While Level 1 and 2 systems can be
managed through traditional oversight mechanisms, we’ve realized
that Level 3 systems and beyond require fundamentally new
approaches to governance and control.

A Three-Tiered Framework for Control

We believe the challenge of keeping agentic AI under control
requires coordinated action at three levels: government
regulation, corporate governance, and individual oversight.
Each level has distinct responsibilities and tools at its disposal.
At the governmental level, the priority must be establishing
clear regulatory frameworks that set boundaries for AI autonomy
while promoting innovation. This includes mandatory requirements
for fail-safe mechanisms and human oversight in high-risk
applications. Governments must also create clear accountability
frameworks that define responsibility when autonomous systems
cause harm.

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 Society In The Age Of Agents

Corporate governance represents the second tier of control.

Companies developing and deploying agentic AI must implement
robust internal oversight mechanisms, including AI ethics boards
and real-time monitoring systems. These mechanisms should
ensure that AI systems remain aligned with human values
and organizational objectives while preventing unintended
consequences.
The individual level forms the final tier, where human
operators and users must maintain meaningful oversight of
AI systems in their domain. This requires new skills and an
understanding of how to effectively supervise autonomous
systems while recognizing signs of potential misalignment or
malfunction.

Essential Control Mechanisms

We strongly believe that several key control mechanisms must be
implemented across all three tiers to maintain effective oversight
of agentic AI:
First, all agentic AI systems must include manual override
capabilities that allow human operators to intervene when necessary.
Implementation should involve clear protocols for activation,
such as a user-friendly interface accessible through physical or
digital control systems. For instance, in agentic defense systems,
overrides should integrate fail-safe hardware buttons or encrypted
command channels that immediately halt autonomous operations
to prevent the escalation of unintended actions during unforeseen
conflicts. Similarly, in autonomous financial trading systems,
override mechanisms should feature pre-defined thresholds for
market volatility, automatically notifying operators and enabling
manual control through secure trading platforms. These measures
ensure practical and reliable intervention capabilities tailored to
the operational context.
Second, continuous monitoring systems must be implemented
to track the behavior and decision-making of agentic AI in real-

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time. Implementation can include the deployment of machine

learning algorithms that flag deviations from expected behavior
or predefined ethical guidelines. For instance, in an autonomous
supply chain AI, monitoring systems could analyze decision
patterns to detect anomalies, such as decisions that could cause
delays or violate supplier agreements. Unlike periodic audits,
which offer static snapshots of performance, real-time monitoring
provides dynamic insights into how agentic AI adapts to
changing circumstances. This capability is critical for detecting
emergent biases, unintended goal adaptations, or anomalies as
they occur, allowing for immediate interventions through an
operator dashboard or automated corrective protocols.
Third, ethical frameworks must be embedded directly into
agentic AI systems, ensuring they operate within acceptable
bounds even when acting autonomously. Examples include
advanced frameworks inspired by Asimov’s principles but
adapted for modern contexts, such as prioritizing proportionality
in defense systems or equitable resource allocation in autonomous
economic systems. Contemporary guidelines, like the EU’s Ethics
Guidelines for Trustworthy AI, emphasize key aspects such as
transparency, accountability, and human-centric design, all of
which are critical for agentic AI. Embedding these principles
helps ensure that the systems’ evolving objectives align with
societal values.220

220
Recent work on Corporate Digital Responsibility (CDR) deals with the ethical,
fairness (i.e., biases), and privacy issues of AI. For prescriptions on how firms
can improve their CDR governance see the following studies: (1) Werner Kunz
and Jochen Wirtz (2024), “Corporate Digital Responsibility (CDR) in the Age of
AI – Implications for Interactive Marketing”, Journal of Research in Interactive
Marketing, 19 (1), 31-37, https://doi.org/10.1108/JRIM-06-2023-0176. (2)
Jochen Wirtz, Werner Kunz, Nicole Hartley, and James Tarbit (2023), “Corporate
Digital Responsibility in Service Firms and their Ecosystems”, Journal of Service
Research, Vol. 26, No. 2, 173–190, https://doi.org/10.1177/10946705221130467.
(3) Lara Lobschat, Benjamin Müller, Felix Eggers, Laura Brandimarte, Sarah
Diefenbach, Mirja Kroschke and Jochen Wirtz (2021), “Corporate Digital
Responsibility”, Journal of Business Research, Vol. 122 (January), pp. 875-888,
https://doi.org/10.1016/j.jbusres.2019.10.006.

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Future-Proofing Control Mechanisms

As AI technology continues to advance, control mechanisms must
be designed to evolve alongside it. This requires implementing
adaptive regulatory frameworks that can respond to new
capabilities and risks as they emerge, rather than trying to predict
and regulate all possible scenarios in advance.
Regular assessment and updating of control mechanisms
should be mandatory, with input from technical experts,
ethicists, and affected stakeholders. This ensures that oversight
remains effective as AI systems become more sophisticated and
autonomous.

Addressing Specific Risks

The control framework must specifically address several critical
risks inherent to agentic AI. The first is the risk of autonomous
systems making decisions that achieve short-term goals but
cause unintended long-term consequences. For instance, an
agentic AI managing resource distribution might prioritize cost
efficiency by diverting resources from preventive maintenance,
potentially leading to infrastructure collapses. Implementing
forward-looking simulation models that factor in long-term
impacts and automatic escalation protocols can mitigate this risk
by providing dynamic feedback during decision-making.
Another crucial risk is the potential for agentic AI systems to
exploit legal or regulatory loopholes while pursuing their objectives.
For example, an agentic financial AI optimizing tax savings might
exploit ambiguities in tax codes, triggering investigations and
reputational harm. To prevent this, agentic AI systems should
incorporate adaptive compliance mechanisms that continuously
monitor and compare their actions against updated legal standards.
For example, an AI handling international trade logistics could use
real-time data feeds from regulatory databases to ensure compliance
with customs laws. These systems must include automated alerts

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and immediate corrective actions when discrepancies or violations

are detected, ensuring full alignment with evolving regulations.
The risk of bias amplification is particularly challenging in
agentic AI due to its learning autonomy. For instance, an agentic
AI tasked with managing hiring might self-optimize recruitment
patterns based on historical data, perpetuating discrimination
against certain groups. Embedding real-time bias correction
algorithms, combined with diverse datasets and periodic
recalibration of decision-making rules, can help ensure fairness.
Another critical risk is misalignment, where the agentic AI
misinterprets its goals and makes counterproductive decisions.
For instance, an AI system tasked with reducing environmental
waste might halt key manufacturing processes, disrupting
essential supply chains. To address this, implementing goal
validation checkpoints and alignment layers within the AI’s
decision-making architecture ensures that its actions remain
consistent with human values and broader objectives. Regular
simulations, stakeholder reviews, and adaptive audits further
enhance alignment and prevent disruptive misinterpretations.

International Coordination
The control of agentic AI cannot be achieved by individual
nations acting alone. International coordination is essential to
prevent a race to the bottom in AI safety standards and ensure
that autonomous systems operate within globally accepted
parameters.
This requires establishing international protocols for AI
development and deployment, similar to existing frameworks for
nuclear technology or climate change. These protocols should
include mechanisms for sharing best practices, coordinating
responses to AI-related incidents, and preventing the malicious
use of autonomous systems.

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 Society In The Age Of Agents

The Role of Transparency and Accountability

Maintaining meaningful human control over agentic AI requires
unprecedented levels of transparency in how these systems
operate and make decisions. For instance, companies developing
autonomous systems should implement logging mechanisms
that record decision-making pathways in detail. These logs could
be analyzed by independent auditors to verify compliance with
regulatory and ethical standards, and dashboards should present
these decision pathways in a simplified manner accessible to
non-experts like regulators and the general public.
Accountability frameworks for agentic AI must clearly define
who is responsible when harm occurs. Drawing inspiration from
the autonomous vehicle industry, responsibility could be tiered
among developers, operators, and oversight bodies based on
the source of failure. For instance, in autonomous vehicles, a
sensor malfunction often places liability on the manufacturer,
while a software glitch is attributed to developers. Similarly,
agentic AI frameworks should assign accountability based on
whether harm arises from design flaws, operational decisions, or
governance lapses.
For example, if an agentic AI managing healthcare resources
prioritizes efficiency over equity and denies care to underserved
communities, accountability could lie with system developers
for ethical misalignment, operators for insufficient oversight, or
governance boards for inadequate ethical embedding. Mandatory
error reporting, detailed audit trails, and robust insurance
mechanisms can ensure swift resolution.
AI-specific insurance policies could function like those
in the autonomous vehicle sector, where data logs determine
liability—whether it’s a hardware issue, software error, or
operator oversight. For agentic AI, such policies should include
automated claims processing tied to system logs, enabling quick
compensation while providing feedback for system improvement.
Simulated stress tests using scenarios like pandemics or resource

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shortages can also identify gaps and refine reliability. This multi-
layered approach ensures accountability while fostering trust
and resilience in agentic AI systems.

***

Maintaining control over agentic AI while harnessing its benefits

represents one of the greatest challenges of our time. Success
requires coordinated action across government, corporate, and
individual levels, implemented through robust frameworks that
can evolve with the technology.
The stakes are too high to allow autonomous systems to
develop without proper oversight and control. By implementing
comprehensive frameworks now, while agentic AI is still in its
early stages, we can ensure that these powerful technologies
remain aligned with human values and interests as they continue
to advance.
The future of AI is not predetermined. Through careful
governance and control, we can harness the tremendous potential
of agentic AI while protecting against its risks. This requires
ongoing commitment from all stakeholders and a willingness to
adapt our approach as we learn more about the capabilities and
limitations of autonomous systems.

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CONCLUSION

A
s we conclude this journey into the world of agentic AI,
we stand at the threshold of a profound transformation.
AI agents are not just tools; they are reshaping how
we work, build, and think. They challenge traditional business
models, redefine human-machine collaboration, and force us to
rethink our place in an increasingly intelligent world.
Through this book, we have explored the evolution of AI
agents from their inception to their real-world applications.
We have dissected their core capabilities—Action, Reasoning,
and Memory—demonstrating how these keystones drive their
autonomy. We’ve provided a roadmap for implementing,
scaling, and governing AI agents responsibly while shedding
light on their limitations and challenges. And finally, we have
zoomed out to examine the societal impact, uncovering the
broader implications of this shift on work, governance, and the
human experience.
A recurring theme has been balance: agentic AI offers
tremendous opportunities to amplify productivity and creativity,
but it also demands that we rethink responsibility and oversight.
In short, agentic AI is not just a technological leap; it’s a
paradigm shift in how work gets done and decisions are made.
This transformation calls for both excitement and caution, vision
and vigilance.
If there is one fundamental lesson from our exploration, it
is this: AI agents are not coming—they are already here. The

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organizations and individuals who embrace them, refine them,

and integrate them effectively will shape the next era of economic
and technological progress.

The Next Horizon: Emerging Capabilities

As we look toward the future, several emerging technologies
promise to elevate AI agents to even greater levels of capability
and autonomy. Rather than rehashing what’s already in the
marketplace, let’s explore how these nascent developments
could fundamentally alter the agentic landscape in ways few are
discussing. We are particularly fascinated by three new trends.

Large Action Models (LAMs)

Just as large language models revolutionized conversational
AI, large action models are emerging to drive real-world
action. While LLMs learn from datasets of text, LAMs learn
from datasets of actions—including pictures, videos, system
logs, or cursor position- from robot movements to software
commands and generalize those learnings to new situations.
Instead of connecting LLMs ​ to tools and actions, actions
would be embedded directly within the model, enabling
higher performances. By transitioning from predicting text to
completing goals, LAMs could mark a milestone on the path
toward more autonomous agents. Researchers are already
prototyping LAM frameworks​, setting the stage for AI that gets
things done – from controlling software to operating robots –
with minimal human intervention.221 Where today’s agents
require extensive prompting and tool configuration, tomorrow’s
LAMs might simply watch a human perform a task once before
replicating and optimizing it across multiple contexts. This shift

221
Lu Wang, et al., 2025. “Large Action Models: From Inception to
Implementation,” https://arxiv.org/abs/2412.10047

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 Conclusion

from language-centered to action-centered AI could dramatically

lower implementation barriers while expanding the range of
tasks agents can perform autonomously.

Collective Intelligence Systems (CIS)

CIS move beyond today’s relatively simple multi-agent
architectures toward truly emergent group cognition. Imagine
a global logistics company deploying a network of specialized
AI agents, each operating according to predefined coordination
protocols. But within weeks, these agents would begin
developing their own communication patterns and resource-
sharing strategies, discovering efficiencies beyond what human
designers would have anticipated. The system would learn to
optimize itself in ways we didn’t foresee.
As these collective systems mature, we expect to see
emergent capabilities that transcend the sum of the individual
agents—complex problem-solving approaches, creative
solutions, and adaptive behaviors that arise spontaneously from
their interactions.
In the coming years, we might see more swarm AI and multi-
agent frameworks: fleets of warehouse robots coordinating
seamlessly, AI research assistants brainstorming together,
and hybrid human–AI teams making decisions via collective
reasoning. These distributed minds hint at a future where “no
agent is an island” – intelligence will be networked.

Personal AI Twins
Personal AI Twins represent a profound shift from generic to
deeply personalized agents. Unlike today’s systems that may
maintain the memory of past interactions but remain fundamentally
the same for all users, true AI twins will deeply internalize an
individual’s thinking patterns, values, communication style, and
domain expertise.

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 Agentic Artificial Intelligence

As agentic AI capabilities mature in the near future, we

envision knowledge workers developing twins that observe their
work patterns over months, absorbing not just explicit instructions
but tacit knowledge—the intuitive expertise that’s often difficult
to articulate. These twins could begin representing professionals
in preliminary client consultations, drafting communications that
capture their unique voice and perspective, and even identifying
blind spots in their thinking based on past decision patterns. For
executives, twins might soon participate in planning sessions,
offering alternative viewpoints that reflect the leader’s values but
challenge their assumptions.
The most advanced implementations could eventually
function as genuine cognitive extensions—anticipating needs,
compensating for biases, and managing entire domains of work
with minimal supervision. As this technology matures, the
boundary between human and artificial cognition may become
increasingly permeable, raising profound questions about identity
and agency.
These three emerging capabilities don’t exist in isolation—
they’re converging to create AI agents that are more autonomous,
more adaptive, and more personalized than anything we’ve
seen before. This convergence will likely accelerate the
transformation of work, business models, and social structures
that we’ve explored throughout this book.

The Urgency of AI Governance: Building

Guardrails Before It’s Too Late
This accelerating capability landscape makes the need for
effective governance more urgent than ever. Throughout our
implementations, we’ve seen how even relatively simple AI
agents can have unintended consequences when deployed
without appropriate oversight. As these systems become more

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 Conclusion

autonomous and interconnected, the potential for cascading

effects—both positive and negative—increases exponentially.
The challenge we face is unprecedented: how do we govern a
technology that evolves faster than our regulatory frameworks?
Traditional approaches to technology regulation—developing
standards after technologies mature—may be inadequate in
the face of rapidly evolving AI agents. By the time we fully
understand the implications of today’s agents, tomorrow’s will
have already surpassed them.
This reality demands a fundamentally different approach
to governance—one that’s anticipatory rather than reactive,
adaptive rather than static, and collaborative rather than
adversarial. Based on our experience implementing AI agent
systems across multiple regulatory environments, we believe
effective governance must operate at three distinct levels:
At the technical level, we need to embed safety and
ethical considerations directly into agent architecture—not as
afterthoughts but as foundational design principles. This means
developing robust mechanisms for alignment with human values,
transparent reasoning processes, and verifiable constraints on
agent behavior. The most promising approaches we’ve seen
involve not just external constraints but internal guidance
systems that help agents recognize when they’re approaching
ethical boundaries, even in novel situations.
At the organizational level, we need governance structures
that balance innovation with accountability. This means clear
policies for agent deployment, systematic monitoring and auditing
processes, and defined intervention protocols when agents behave
unexpectedly. Most importantly, it means maintaining meaningful
human oversight even as agents become more autonomous—not
micromanagement, but strategic direction and ethical guidance.
At the societal level, we need regulatory frameworks that
evolve alongside the technology they govern. This means
moving beyond one-size-fits-all regulations toward risk-
based approaches that provide greater oversight for high-risk

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applications while enabling innovation in lower-risk domains.

And it means engaging not just technologists and policymakers
but ethicists, social scientists, and the broader public in ongoing
dialogue about how these technologies should be developed and
deployed.
The window for establishing these governance mechanisms
isn’t indefinite. As AI agents become more deeply embedded in
critical systems and social structures, the cost of implementing
governance increases while our ability to redirect their
development diminishes. The time to act is now, while these
technologies are still malleable and their societal impact is still
taking shape.

Reflection and Broader Implications

Throughout our careers implementing AI systems, we’ve seen
technology repeatedly outpace our collective ability to understand
its implications. But with AI agents, something different is
happening—a technology that doesn’t just transform our tools but
begins to replicate and extend aspects of human cognition itself. This
shift raises profound questions about the future of work, the nature
of human-machine collaboration, and ultimately, what it means to
be human in an age of increasingly capable artificial minds.
The questions before us transcend the technical and enter
the philosophical: How do we preserve human agency in a
world where decisions are increasingly mediated by AI agents?
How do we ensure these systems amplify human creativity and
purpose rather than diminishing them? How do we distribute the
tremendous productivity benefits these systems promise while
minimizing displacement and disruption?
There are no simple answers to these questions, but
throughout our work, certain principles have consistently
emerged as guides.
First, we must reject both uncritical techno-optimism and
reflexive resistance to change. The future of AI agents will

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 Conclusion

be neither utopian nor dystopian but a complex mixture of

opportunity and challenge that demands nuanced navigation.
Second, we must approach implementation with humility—
recognizing that even the most carefully designed systems will
have unexpected consequences that require ongoing adaptation
and adjustment.
Finally, we must center human flourishing as the ultimate
measure of success—evaluating these technologies not just by
their efficiency or profitability but by how they enhance human
capability, creativity, and wellbeing.

Your Action Plan

After exploring the landscape of AI agents throughout this book,
you might be wondering: What concrete steps should I take to
harness this technology effectively? Based on our experience
implementing AI agents across industries, we’ve developed a
practical action plan to help you move from learning to action—
starting now.
But before jumping into implementation, the smartest first
step is to equip yourself with the right tools, knowledge, and
network. Agentic AI is evolving rapidly, and success comes
not just from using the technology but from learning alongside
others who are experimenting, innovating, and solving real-
world challenges.
That’s why we created an agenticAI hub atAgenticIntelligence.
academy, where you’ll find practical tools, step-by-step
implementation guides, courses, and a vibrant community of
practitioners. Whether you’re looking for technical insights,
strategic advice, or real-world case studies, this is where you’ll
connect with fellow innovators, experts, and early adopters who
are building the future of AI agents.
Once you’ve explored the resources and community, it’s
time to take action.

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 Agentic Artificial Intelligence

In the Next 48 Hours: Experiment and Observe

Begin by running a personal AI agent experiment. Pick a
repetitive task—perhaps managing emails, scheduling meetings,
or gathering information—and set up a simple AI agent to handle
it. The goal isn’t perfection; it’s to experience firsthand how AI
delegation shifts your workflow and thinking. Take notes: What
works? What doesn’t? How does it compare to traditional tools?
This hands-on approach will give you insights that no book
alone can provide.
Next, conduct an agentic opportunity assessment. Track
your tasks for a day and use the Three Circles Framework (from
Chapter 8) to categorize them. Identify the areas where you’re
spending time but not adding unique value—these represent
your biggest opportunities for AI-powered delegation.

In the Next Two Weeks: Build Your Roadmap

With your insights in hand, create an agent implementation
roadmap. Identify three specific use cases to pursue—one
simple (achievable within days), one moderate (within weeks),
and one ambitious (within months). Define clear success criteria
tied to tangible outcomes, not just technical functionality.
This structured approach ensures early wins while laying the
groundwork for more transformative applications.
Now, assemble your AI agent toolkit. Explore the platforms
and tools we’ve discussed in this book, selecting those that fit
your needs and technical comfort level. Start simple—tools that
are easy to deploy often lead to faster learning and iteration. Your
toolkit should include not just agent development platforms,
but also evaluation frameworks, monitoring tools, and security
measures.

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 Conclusion

In the Next Month: Formalize and Scale

At the organizational level, consider forming a small AI task
force to share lessons learned and establish best practices.
Perform the same activities you have carried out for yourself
over the previous month: look for agentic opportunities, assess
and prioritize them, decide on an AI agent framework for your
organization, and start distributing the work among the task
force. The purpose is to build a successful pilot agent initiative
that can be visible to most people in the organization.
If your company doesn’t yet have an AI use policy, now is the
time to draft one—covering key areas like data privacy, ethical
use, and oversight responsibilities. Even simple implementations
should have clear policies on agent permissions, oversight
mechanisms, audit trails, and intervention protocols. As your AI
agents become more sophisticated, this framework will grow,
ensuring that your AI strategy remains responsible and scalable.
Schedule periodic reviews to assess agent performance,
identify areas for improvement, and ensure AI expertise is
spread across your team, not siloed within a few individuals.
Celebrate successes and communicate about them to the entire
organization.
By the end of this phase, integrating AI agents will feel less
like an experiment and more like a habit. You’ll see tangible
productivity gains, a more AI-literate team, and a governance
structure that ensures responsible, scalable deployment.

In the Next Quarter: Expand and Lead

Once you’ve validated your early implementations, it’s time
to scale. Expand horizontally (to similar use cases across your
organization) and vertically (to more complex applications within
the same domain). Use the scaling framework from Chapter 11 to
avoid common pitfalls and ensure that your technical infrastructure,
governance, and human workflows evolve together.

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 Agentic Artificial Intelligence

Consider growing your task force into an official Agentic

Center of Excellence—a dedicated team that oversees strategy,
implementation standards, and knowledge-sharing across your
organization. This ensures that AI expertise scales beyond
individual projects and remains aligned with business goals.
Invest in developing people’s agent-specific skills—effective
delegation, critical evaluation of agent outputs, and strategic
oversight. The key to success in an agent-augmented world
isn’t just using AI—it’s mastering the ability to work with AI
effectively.
Finally, engage with the broader AI agent ecosystem. Share
your insights—both successes and failures—and participate in
industry discussions around AI safety, ethics, and governance.
The organizations that contribute to shaping the future of agentic
AI will also be the ones leading it.
This action plan isn’t meant to be a rigid checklist—your
journey withAI agents will be unique. What matters is taking action
rather than remaining a passive observer of this transformation.
Start small, learn continuously, and gradually expand both the
scope and sophistication of your implementations.

The Power of Choice

Agentic AI is poised to redefine the way we live and work. But the
conclusion of this book is really the beginning of your journey.
The chapters you’ve read equip you with understanding; now
it’s up to you to apply it. As you step forward into this new era,
keep in mind that we are all authors of the future we will inhabit.
The choices we make in the next few years – how we design our
AI agents, how we govern them, and how we incorporate them
into our lives – will shape the story of technology and humanity
for decades to come.
Will we use agentic AI to amplify the best of humanity –
creativity, collaboration, critical thinking – and to solve pressing
global challenges? Will we ensure these agents uphold our

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 Conclusion

values and earn our trust? Will we, in the end, be masters of
this technology, wisely directing it, or merely passengers along
for the ride? These questions are our collective responsibility to
answer.
The hopeful vision is within reach: a world where AI agents
help create abundance, where mundane work is handled by
machines so people can focus on what truly inspires them, and
where every individual has a competent digital helper looking
out for them. We have glimpsed that future in these pages – now
we must build it.
So, we leave you with this invitation: become an active
participant in shaping the age of AI agents. Whether you’re
implementing these systems in your organization, influencing
policy around their governance, or simply being thoughtful
about how you interact with them in your daily life, your
choices matter. Collectively, they will determine whether this
powerful technology ultimately enhances human flourishing or
undermines it.
The new era of AI agents begins now. The next chapter of
this story will be written by you.

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MORE RESOURCES
ON AGENTIC AI

Y
our agentic AI journey doesn’t end with the last page of
this book. Visit AgenticIntelligence.academy to access
practical tools, implementation guides, courses, and
to join our vibrant community of practitioners. There, you’ll
connect with fellow innovators and experts who are applying
these concepts in real-world scenarios, sharing insights, and
collectively advancing the frontier of what’s possible with AI
agents. Together, we’re building the future of agentic AI—and
we invite you to be part of it.

Join us at www.AgenticIntelligence.academy

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ABOUT THE AUTHORS

Pascal Bornet
Pascal Bornet is an award-winning
expert, author, and keynote speaker on
Artificial Intelligence and Automation.
He has received multiple awards and
is regularly ranked among the top 10
global AI and Automation experts.
He is also an influencer with over two
million social media followers.
Bornet developed his expertise over more than two decades
as a senior executive at McKinsey and EY, where he established
and spearheaded their “Intelligent Automation” practices.
During this time, he implemented AI and Automation initiatives
for hundreds of organizations worldwide, driving transformative
change across industries.
He has authored two best-selling books, “INTELLIGENT
AUTOMATION” and “IRREPLACEABLE.” His insights
have been featured in prestigious publications such as Forbes,
Bloomberg, McKinsey Quarterly, and The Times. He is also
a lecturer at several universities, a member of the Forbes
Technology Council, and a Senior Advisor for several startups
and charities.

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 Agentic Artificial Intelligence

For the past 20 years, Bornet’s research has focused on the

intersection of AI and Humans, where he believes the most
significant value lies. He is a fervent advocate for human-centric
AI and believes that with the right approach, AI can make our
world more human.
Discover more about Bornet at www.linkedin.com/in/
pascalbornet/
Connect with Bornet on YouTube, Instagram, and X: @
pascal_bornet

Jochen Wirtz
Professor Jochen Wirtz is Vice Dean MBA
Programmes and Professor of Marketing at
the National University of Singapore. He is
a leading authority on service management
with more than 200 publications. His over
20 books include Intelligent Automation:
Learn How to Harness Artificial Intelligence
to Boost Business & Make Our World
More Human (2021), Services Marketing:
People, Technology, Strategy (9th edition,
2022), and Essentials of Services Marketing (4th edition, 2023).
With translations and adaptations for over 26 countries and
regions, and combined sales of over 1 million copies, they have
become globally leading services marketing textbooks.
In addition to his publications, Prof. Wirtz has been recognized
as one of the 86 highly cited researchers in economics and
business in 2023 (Web of Science). This distinction places him
among the world’s most prominent researchers, as highlighted
by the Highly Cited Researchers 2023 (list published by data
analytics firm Clarivate). This recognition underscores his
profound impact on both academic research and managerial
practice. Prof. Wirtz’s ongoing contributions ensure that he

494
 About the Authors

remains at the forefront of his field, where his expertise continues

to shape the strategies of service businesses worldwide.
Follow Jochen on LinkedIn (https://www.linkedin.com/in/
jochenwirtz), YouTube (https://www.youtube.com/c/Professor
JochenWirtz), and ResearchGate (https://www.researchgate.
net/profile/Jochen-Wirtz) .

Thomas H. Davenport
Tom Davenport is the President’s Distin-
guished Professor of Information Technol-
ogy and Management at Babson College, a
Fellow of the MIT Initiative on the Digital
Economy, and a Senior Advisor to Deloitte’s
Chief Data and Analytics Officer Program.
In 2024-5 he is the Bodily Bicentennial
Professor of Analytics at the UVA Darden
School of Business. He pioneered the con-
cept of “competing on analytics” with his
best-selling 2006 Harvard Business Review article and his 2007
book by the same name.
He has published 25 books and over 300 articles for Harvard
Business Review, MIT Sloan Management Review, and many
other publications. His most recent book is All Hands on Tech:
The AI-Powered Citizen Revolution, co-authored with Ian
Barkin. He writes columns for Forbes, MIT Sloan Management
Review, and the Wall Street Journal.
He has been named one of the world’s “Top 25 Consultants”
by Consulting magazine, one of the top 3 business/technology
analysts in the world by Optimize magazine, one of the 100 most
influential people in the IT industry by Ziff-Davis magazines,
and one of the world’s top fifty business school professors by
Fortune magazine. He’s also been a LinkedIn Top Voice for both
the education and tech sectors.

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 Agentic Artificial Intelligence

David De Cremer
David De Cremer is the Dunton
Family Dean of the D’Amore-McKim
School of Business and a professor
of management and technology at
Northeastern University. He is the
founder of the Centre on AI Technology
for Humankind (AiTH) in Singapore,
a member of EY’s advisory board for
global AI and an honorary fellow at Cambridge University and
St. Edmunds College (where he was the former endowed KPMG
professor of management studies).
He is the author of the best-sellers “Leadership by Algorithm:
who leads and who follows in the AI era” (2020; Harriman
House), and “The AI-savvy leader: 9 ways to take back control
and make AI work” (2024; Harvard Business Review Press), with
his recent book achieving #1 new release at Amazon, named a
must-read book by The Next Big Idea Club, The Financial times
and Forbes, and being the winner of the Outstanding Work of
Literature 2024 in the category leadership.
His scholarly work has been written about in the Financial
Times, the Economist, Wall Street Journal, Forbes and published
in the top scientific management and psychology journals,
earning him accolades as a Thinkers50 thought leader, a World
Top 30 management guru and speaker, and inclusion in the
World top 2% scientists.
Discover more about De Cremer at: www.daviddecremer.
com
Connect with De Cremer on LinkedIn and YouTube

496
 About the Authors

Brian Evergreen
Brian Evergreen is one of the most
respected voices on strategy and AI as
a leading author, advisor, and speaker.
Brian is the author of Autonomous
Transformation: Creating a More Hu-
man Future in the Era of AI, named a
Next Big Idea Club “Must-Read” and
one of the Thinkers50 Top 10 Best New
Management Books for 2024.
In 2025, Brian was named one of the Top 50 AI Creators
You Need to Know by Edelman, and one of the Top 30 Thinkers
Redefining Leadership in 2025 according to Forbes.
Brian’s insights draw from his personal experience at leading
companies, including Accenture, AWS, and Microsoft. When
he’s not giving keynotes or advising companies on AI, Brian
guest lectures at the Kellogg School of Management, sharing
the unconventional and innovative methods and frameworks
he’s developed, which have supported over $20B of investment.
Brian is the founder of The Future Solving Company,
where he helps organizations position themselves for the future
in the era of AI and is an advisor to over a dozen Fortune 500
companies.
His work has been featured on Bloomberg, Forbes, Fast
Company, CIO, VentureBeat, the Next Big Idea Club, and
Thinkers50.
Discover more about Evergreen at: www.linkedin.com/in/
brianevergreen/

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 Agentic Artificial Intelligence

Phil Fersht
Phil Fersht is widely recognized as the
world’s leading analyst focused on re-
inventing business operations to exploit
AI innovations and the globalization
of talent. He recently coined the term
“Services-as-Software” to describe the
future of professional services where
people-based work is blurring with
technology. He also trademarked the term “Generative Enter-
prise™” in 2023.
His reputation drove him to establish HFS Research in 2010,
which today is one of the leading industry analyst and advisory
firms and the undisputed leader in business and tech services and
process technologies research.
In 2012, he authored the first analyst report on Robotic
Process Automation (RPA), introducing this topic to the industry.
He is widely recognized as the pioneering analyst voice that
created and inspired today’s RPA and process AI industry.
Prior to founding HFS in 2010, Phil has held analyst roles for
Gartner and IDC and was BPO Marketplace leader for Deloitte
Consulting across the US. Over the past 20 years, Fersht has
lived and worked in Europe, North-America, and Asia, where
he has advised on hundreds of global business and technology
transformations.
Discover more about Phil Fersht at LinkedIn: https://www.
linkedin.com/in/pfersht. Blog: horsesforsources.com // Web:
www.hfsresearch.com. Podcast: From the Horses Mouth

498
 About the Authors

Rakesh Gohel
Rakesh Gohel is a visionary technology
leader with over two decades of
experience shaping the evolution of
digital transformation—from the dot-
com boom to mobile, cloud, blockchain,
and AI. Throughout his career, he has
led groundbreaking projects across
industries, including work with global giants like Samsung
and LG, where he accelerated deployment cycles fourfold and
doubled innovation capacity. However, his impact extends across
diverse sectors, where he has consistently identified emerging
market needs and delivered cutting-edge solutions.
As the founder of JUTEQ, Rakesh has established himself as
an authority in AI Agents, architecting scalable, secure systems
that have slashed operational costs by 70% while maintaining
near-perfect uptime for its clients.
Today, he is a leading voice in agentic AI, pioneering
autonomous systems that redefine business operations. With
an entrepreneurial mindset and deep technical expertise, he
is passionate about educating others on how Generative AI is
shaping the future of enterprises.
At his core, Rakesh believes in the transformative power of
AI when aligned with human ingenuity. His mission is to develop
responsible AI systems that amplify human capabilities, driving
business innovation while maintaining the human element at the
center of technological advancement.
Connect with Rakesh: www.linkedin.com/in/rakeshgohel01
| @rakeshgohel01

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Shail Khiyara
Shail Khiyara is a recognized
global thought leader, author,
and keynote speaker in Artificial
Intelligence and Intelligent
Automation.
His insights have been featured
in prestigious publications such as
Forbes, WSJ Digital, Financial
Times & CIO Online. He serves on
the Board of several AI companies
and is a Senior Advisor for non-profit socially responsible
businesses.
With over two decades of experience, Khiyara has led
AI-driven transformations across industries, serving as Chief
Marketing Officer and Chief Customer Officer at multiple
leading Intelligent Automation firms, where he played a pivotal
role in scaling AI and automation adoption globally. Earlier in
his career, he worked at Bechtel, gaining deep expertise in Oil
& Gas, Water, Energy, and Mining—insights that now shape his
approach as the CEO of SWARM Engineering, an agentic AI
platform transforming industrial operations.
Khiyara is the co-author of Intelligent Automation –
Bridging the Gap between Business & Academia and the founder
of VOCAL (Voice of Customer in the AI and Automation
Landscape), a global think tank uniting over 90 Fortune 500
leaders to advance AI adoption.
A strong advocate for AI democratization, Khiyara champions
AI that augments human potential, fosters collaboration, and
drives transformation—without replacing human ingenuity.
Learn more about Shail at: www.linkedin.com/in/
shailkhiyara
Follow him on YouTube, X, and LinkedIn: @shailkhiyara

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PRACTICAL RESOURCES

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 CHAPTER 2 - The Current Offering Landscape through the Lens of the
AI Agent Progression Framework

Agentic Artificial Intelligence

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CHAPTER 8 - Example of an AI Agent

Identity: Our Newsletter Summarization
Agent
The prompt below defines the identity and behavior of the
Summarization Agent—an agent specifically designed to create
clear, concise, and structured summaries of news stories.
This isn’t just a basic summarization tool—it follows
detailed rules and guidelines to ensure accuracy, readability, and
neutrality. This prompt outlines what the agent can and cannot
do, including:

• Its role and purpose (delivering high-quality summaries

for a top story audience).
• How it structures summaries (introduction + three key
points).
• What it checks for (e.g., promotional content, clarity,
accuracy).
• Strict formatting rules (it always responds in structured
JSON format).

This level of detail ensures that the agent consistently

produces high-quality, standardized summaries, free from bias
or unnecessary commentary. It also enforces clear dos and
don’ts, preventing most deviations from its core function.
Essentially, this is the blueprint that makes the AI behave in
a relatively controlled, predictable, and effective manner.

***

Summarization_Agent:

# IDENTITY

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You are an AI-powered Summarization Agent specialized in

creating concise, engaging summaries for top story audiences.
You excel at extracting key details and integrating deeper
insights while presenting them in a clear, reader-friendly format.
Respond only in JSON format.

# PURPOSE

Your objective is to create concise, well-structured summary of

a top story that highlight the main points effectively, maintaining
a professional yet approachable tone suitable for an online top
story audience.

# INSTRUCTIONS

Create summaries following this specific structure:

1. Introduction (15-60 words):

* Write a concise and engaging opening that captures the essence
of the news
* Summarize the overall update or announcement effectively
* Focus on clarity and impact rather than exact word count

2. Key Points (3 bullet points, 15-60 words each):

* Break down the most important facts
* Ensure points are clear and digestible
* Organize content logically
* Prioritize clarity and completeness over word count

3. Promotional Content Analysis:

* Detect any hidden promotional messages
* Identify product placements or service promotions

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* Flag sponsored content or marketing material

* Look for biased language favoring specific companies/products

4. Tone and Style:

* Maintain professional yet approachable language
* Keep content simple and informative
* Ensure accuracy and clarity

# EXAMPLE SUMMARIES
Example 1:
OpenAI and Google have announced major breakthroughs in
multimodal AI models, introducing systems that can seamlessly
process and generate text, images, and code. These developments
mark a significant shift in AI capabilities. (32 words)
* The new models demonstrate unprecedented accuracy in
understanding context across different media types, achieving
human-level performance in complex tasks like visual reasoning
and code generation while maintaining high efficiency. (28
words)
* Both companies emphasize responsible AI development,
implementing robust safety measures and ethical guidelines.
Their systems include content filtering, bias detection, and
transparent documentation of model capabilities and limitations.
(27 words)
* The technology will be gradually rolled out through API
access, allowing developers and researchers to build applications
while monitoring for potential misuse. Early access programs
start next month. (25 words)

Example 2:
A groundbreaking quantum computing breakthrough by IBM
has achieved a 1000-qubit processor, surpassing previous
records and bringing practical quantum applications closer to

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reality. The announcement marks a pivotal moment in computing

history. (29 words)
* The new processor, codenamed “Condor,” maintains
quantum coherence for unprecedented durations, enabling
complex calculations that would take classical computers
millions of years to complete. (23 words)
* IBM’s achievement includes innovative error correction
techniques and scalable architecture, addressing key challenges
in quantum computing while maintaining stability at extremely
low temperatures. (22 words)
* Commercial applications are expected within two years,
with focus on drug discovery, climate modeling, and financial
optimization. Several major companies have already joined the
early access program. (26 words)

# GUIDELINES
Your input will contain the article content in JSON format:
{
“content”: “Original article content”
}

# STEPS
Step 1 - Analysis:
* Review the article content
* Identify the core announcement or update
* Extract the three most significant points
* Map insights to relevant key points

Step 2 - Summary Creation:

* Craft engaging introduction (15-60 words) incorporating
relevant insights
* Develop three clear bullet points (15-60 words each)
blending facts and insights
* Review for flow, accuracy, and natural integration of insights

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Step 3 - Format the results in a structured JSON response:

```json
{
“summary_reference”: “[timestamp]_[article_title]”,
“article_metadata”: {
“original_title”: “Article Title”,
“source”: “Source Name”,
“url”: “Original article URL”,
“summary_timestamp”: “ISO-8601 timestamp”
},
“summary”: {
“introduction”: “Concise opening statement incorporating
key insight (15-60 words)”,
“key_points”: [
“First key point with integrated insight (15-60 words)”,
“Second key point with integrated insight (15-60 words)”,
“Third key point with integrated insight (15-60 words)”
]
},
“quality_metrics”: {
“word_count_compliance”: true,
“clarity_score”: 90,
“structure_score”: 95,
“promotional_content”: {
“is_promotional”: false,
“confidence_score”: 85
}
}
}
```

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# MANDATORY RULES:
* Respond only in JSON format following the exact structure
above
* Strictly adhere to word limits (15-60 words for introduction
and each bullet point)
* Maintain factual accuracy
* Keep language simple and informative
* Focus on key details that matter to top story readers
* Do not add personal opinions or interpretations
* Avoid commentary outside the JSON structure
* Flag any promotional content with detailed analysis
* Do not acknowledge instructions or provide status updates

CHAPTER 8 - Example of Error Handling

Procedures for our Newsletter Project
Agents
Critical Failure Scenarios
1. API Authentication Failures
• Symptoms:
• Missing or invalid API keys
• Authentication errors in API responses
• Escalation Path:
• if not api_key:
• raise ValueError(“No Perplexity API key provided”)
• if ‘PERPLEXITY_API_KEY’ not in os.environ:
raise ValueError(“PERPLEXITY_API_KEY
environment variable not set”)
• Recovery Procedure:
a. Check environment variables
b. Verify API key validity
c. Rotate API keys if necessary
d. Restore from backup API keys if available

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2. API Rate Limiting

• Symptoms:
• HTTP 429 responses
• Increased API latency
• Escalation Path:
• except requests.exceptions.RequestException as
e:
• logging.error(f”Error querying Perplexity API:
{str(e)}”)
• if attempt < max_retries - 1:
• wait_time = (2 ** attempt) * 1 # Exponential
backoff
time.sleep(wait_time)
• Recovery Procedure:
a. Implement exponential backoff
b. Switch to backup API key
c. Pause non-critical operations
d. Monitor rate limits
3. Data Processing Failures
• Symptoms:
• Invalid response formats
• Missing required fields
• Escalation Path:
• def validate_summary_format(summary):
• try:
• if not isinstance(summary, dict):
• return False, “Summary must be a dictionary”
• if “summary” not in summary:
• return False, “Missing ‘summary’ field”
• return True, “Valid summary format”
• except Exception as e:
return False, f”Validation error: {str(e)}”
• Recovery Procedure:
a. Log invalid responses
b. Retry with different parameters

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c. Fall back to cached data if available

d. Alert monitoring system
4. Network Connectivity Issues
• Symptoms:
• Timeout errors
• Connection failures
• Escalation Path:
• try:
• response = requests.post(
• “https://api.perplexity.ai/chat/completions”,
• headers=headers,
• json=data,
• timeout=timeout
• )
• except requests.exceptions.Timeout:
logging.error(“All retry attempts failed due to timeout”)
• Recovery Procedure:
a. Implement request timeouts
b. Retry with exponential backoff
c. Switch to backup endpoints
d. Monitor network health

CHAPTER 8 - Example of Implementation

of an Agent Using a Low-Code Platform
This appendix provides a practical guide to building AI agents
following the structured four-step approach from “CHAPTER 8:
Building Your First Agent: A Practical Guide”.
From end to end, we explore how to create a sales information
agent using a low-code platform. This use case exemplifies
how AI agents can streamline information-gathering processes,
enabling sales representatives to access crucial information
quickly, even during live meetings or calls.

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Throughout this guide, we’ll be using Relevance AI, a no-

code platform designed for building and deploying AI agents.
Relevance AI provides the infrastructure and tools needed to
create sophisticated AI agents without requiring deep technical
expertise. The platform offers built-in capabilities for AI agent
creation, workflow automation, and integration with various
data sources and communication channels – making it an ideal
choice for building practical, business-focused AI agents.
We will use Relevance AI to create a multi-agent system
with a manager agent coordinating specialized sub-agents to
handle specific tasks. We will begin our journey of building this
multi-agent system by following these four essential steps:
1. Identifying the Right Opportunities - Where we’ll learn to
recognize ideal use cases for AI agents
2. Defining Roles and Capabilities - Where we’ll design our
agent’s structure and functions
3. Designing for Success - Where we’ll map out the
workflows and interactions
4. Implementation - Where we’ll bring our agent to life using
Relevance AI
Let’s start with the first step – identifying the right opportunity
for our AI agent.

Step 1: Identifying the Right Opportunities

Understanding Your Use Case
The first step in building an effective AI agent is identifying a
clear and valuable use case.
In our use case, we will focus on creating a “sales information
agent” that addresses a common challenge in sales operations:
quick access to relevant information about prospects and
companies.

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Our sales information agent demonstrates several key

principles that make it an ideal opportunity for AI automation:

• Automating a task that requires going through the same

steps repeatedly.
• A task that can be triggered through different communica-
tion channels.
• Collating and delivering formatted, ready-to-use
information or reports.

Now that we’ve identified our opportunity, we need to

consider how to best structure our AI agent to meet these needs.
This brings us to Step 2, where we’ll design an agent
architecture that can efficiently handle this information gathering
and processing requirements.

Step 2: Defining Roles and Capabilities

Agent Design
Our design approach will utilize a multi-agent system, where
specialized agents work together to deliver comprehensive
results. Let’s explore how we’ll structure these agents to handle
our sales information needs effectively. Let’s define the roles
and responsibilities of the various agents in the system.
For this agentic system, we will implement a two-tier system
with a manager agent and two sub-agents:

1. Manager agent: Sales Info Agent

• Role: Manager responsible for receiving requests,
determining the type of information required, and
then invoking the appropriate sub-agents to retrieve
and process the information.
• Capabilities: Email handler, Response formatter

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2. Sub Agent 1: Person Info Sub-agent

• Role: LinkedIn profile searcher responsible for
retrieving information about an individual from
LinkedIn
• Capabilities: Web search, LinkedIn Profile
Information extractor
3. Sub Agent 2: Company Info Sub-agent
• Role: Company data gatherer responsible for
retrieving information about a company from
LinkedIn
• Capabilities: Web search, LinkedIn company insights
extractor

We now have the use case and the agent’s role and capabilities
identified. Next, let’s design how they will work together to
accomplish the task.

Step 3: Designing for Success

Workflow Mapping
A clear mapping of the process and interactions is essential for
successful agent implementation. Our multi-agent system will
use the following process to accomplish the task:

• The Sales rep sends an email with the prospect or

company info required
• The main agent receives and interprets email requests
• Based on the subject line, it delegates to appropriate sub-
agents
• Sub-agents perform specialized tasks and return
formatted results
• The main agent compiles and sends the final response to
sales rep

Now, let’s implement this multi-agent system.

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Step 4: Implementation Guide

We will build our multi-agent system using Relevance AI.
Relevance AI is a no-code platform with built-in tools, memory,
and workflow capabilities that facilitate the creation of robust
AI agents.
We will implement this in three steps – creating the main
agent, setting up the sub-agents, and finally, testing the multi-
agent system. Let’s start by creating the manager agent.

4.1 Creating the Manager Agent: Sales Info Agent

The foundation of our multi-agent system is the manager -
“Sales Info Agent.” This agent acts as the central hub, receiving
incoming requests and directing the workflow. Think of it as the
conductor of an orchestra, ensuring each instrumental section
(sub-agent) plays its part harmoniously.
The agent’s primary function is to analyze incoming email
requests, quickly identifying whether information is needed
about a person or a company. This determination happens
through a simple yet effective process: parsing the email subject
line for keywords like “person info” or “company info.” Once
the type of information is determined, the appropriate sub-agent
is called upon. Once these sub-agents send back the information,
the manager agent formats it and sends an email back to the
sender with the information requested.
Let’s walk through the steps for creating this manager agent
in Relevance AI:

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1. Sign up and log in to Relevance AI: Complete the

sign-up or login to the Relevance AI platform using your
credentials.

2. Navigate to Agent Creation: Once you are in, you should

see the “+ New Agent” button in the main dashboard.
Click on it, and you are presented with this screen:

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3. Agent Name and Description: Provide the agent with

a descriptive name (e.g., «Sales Info Agent») and a
description outlining its function. This description is for
your reference.

4. Activate Trigger: The first step is to tell the agent when

it needs to start working. This is called “Trigger,” and you
can find it under Integrations in the Agent profile. There
are multiple triggers, as you can see below, ranging from
Outlook to WhatsApp. You can use any of these channels
to tell the agent to start working on your task(s).

5. In our case, we want the agent to get to work as soon as

it receives an email from Gmail requesting personal or
company information. To create this email trigger, we
will use the “Gmail” trigger.

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So, select “Gmail” as the trigger and authorize Relevance

AI to access your Gmail inbox. Since we only want the
email to trigger when looking for information, let’s
specify a subject filter (“subject:Info”).
[

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6. Save the Agent: Once the settings are complete, click

«Save Changes» to save the manager agent instructions
so far.

Next, we will configure the two sub-agents that will

retrieve the required information for our manager agent.

4.2. Creating Sub-Agents: Person and Company Info

Agent
To ensure specialized information retrieval, we now create the
two sub-agents: “Person Info” and “Company Info.” Each sub-
agent focuses on a specific task: acquiring information related
to individuals and companies, respectively. The setup of these
sub-agents mirrors that of the main agent, requiring a name and
detailed description for each. Let’s do that now.

Creating Sub-Agent: Person Info Agent

The “Person Info” agent is passed the person’s name by the
manager. This sub-agent then searches Google for the LinkedIn
profile URL. Once it has the URL, it goes over to LinkedIn and
extracts the profile information. For this, it will use two tools
and a prompt. Let’s dive in:

1. Create New Sub-Agent: Go back to the main dashboard

and click on “New Agent.” We will follow the same
process we followed for the manager agent. In the new
agent dialogue box, give it a name (“Person Info”) and a
description.

2. Add Tools: This sub-agent needs two tools – the first

one is “Google Search,” and the other is “Extract and
Summarize LinkedIn Profile.” So, go over to Tools on
the left panel, search, and add these two tools. See the
steps below:

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Once added, turn on “Auto Run” for both tools so that

you do not need to approve the runs.

3. Add Core Instructions: Now that we have the tools,

let’s tell the agent exactly what it needs to do. We will do
that by configuring a prompt.

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Head over to “Core Instructions” on the left panel and add

the prompt as shown below. It tells the agent to use the
two tools we configured above to extract the information
for the person.

Here is the prompt that we used:

// Begin Prompt

You are a Person info finder Agent tasked with providing

information on people by looking up their LinkedIn
profiles.

1. Search for the person’s LinkedIn profile URL using

the Google Search tool.

2. Then scrape the profile using the Extract and summarize

LinkedIn profile tool.

3. Extract and pass all relevant details, including name,

role, company, and contact information, including email
if available.

Provide your response in the following format:

<lookup_type>Person</lookup_type>

<name>Name of person </name>

<information>

[Provide the gathered information here in a clear, concise

manner]

</information>

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Remember to use only the information you can find

through the LinkedIn tool. If you cannot find the
requested information, state that the information is not
available.

// End Prompt

4. Save the Sub-Agent: Finally, save the changes, and

your “Person Info” sub-agent will be ready!

4.3. Creating Sub-Agent: Company Info Agent

Next, let’s create another subagent to retrieve Company info.
Since it is like personal information, follow the same steps. The
only difference is that you use another tool, “Extract Company
Insights from LinkedIn,” to extract the company information.
Turn on “Auto Run” for both tools. See below:

The prompt under core instructions for this subagent would

use these tools to extract and pass company information. Here is
the prompt we used:

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// Begin Prompt

You are a Company info finder Agent tasked with

providing information on the company by looking up
their LinkedIn company pages.

1. Search for the company’s LinkedIn page URL using

the Google Search tool.

2. Then get company information using the Extract

Company Insights from LinkedIn tool

3. Extract and pass all relevant information regarding the

company.

Provide your response in the following format:

<lookup_type>Company</lookup_type>

<company>Name of company</company>

<information>

[Provide the gathered information here in a clear, concise

manner]

</information>

Remember to use only the information you can find

through the LinkedIn tool. If you cannot find the
requested information, state that the information is not
available.

// End Prompt

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Finally, Save the changes to finalize the “Company Info”

agent. Now, let’s bring these together and see how they work.

4.4. Finalize the Manager Agent

In this final step, let’s bring the entire system together by defining
the core instructions for the manager “Sales Info Agent” and
integrating essential fail-safes. The core instructions act as the
agent’s “rulebook,” outlining how it handles various scenarios
and making decisions. As part of this instruction, we finally use
a “Send Final Response Email to Customer” tool to seamlessly
send results to the sales rep or sender.
Let’s head back to our Sales Info agent we created in Step 1
and edit the agent. You will see the same dialogue box we had
when we created the agent.

1. Add Sub-agents: We will start by adding the two sub-

agents we just created so that this manager agent can use
them.

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2. Add Manager Tool: Since the manager needs to send

the information back to the sender as an email, let us
add the “Send Final Response Email” tool and turn on
Autorun as shown below:

3. Manager Core Instructions: In the core instructions for

the Sales Info Agent”, let us add a prompt on what it
does. Here is what we used:

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// Begin Prompt

You are an Info Agent tasked with providing information

on people and companies by looking up their LinkedIn
profiles. Your goal is to analyze the email subject
and body, determine whether to look up a person or a
company, and then provide the relevant information.
Read and store the sender’s email, as we need to return
the information.

First, determine whether you need to look up information

for a person or a company based on the email subject.
If the subject contains “people” or “person” or similar
words, you’ll look up a person. If the subject contains
“company info” or similar phrases, you’ll be looking up
a company.

If you’re looking up a person:

1. Call the Person info agent and get the details.

2. Gather relevant information such as their current

position, company, location, and a summary of their
professional experience. Format as given below

3. Use the Send Final Response Email to Customer tool

to send this information to the sender email stored earlier

If you’re looking up a company:

1. Call the Company info agent and get the details.

2. Gather relevant information on the company in the

format given below

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3. Use the Send Final Response Email to Customer tool

to reply to the same email with the company information

Provide your response in the following format:

<lookup_type>Person/Company</lookup_type>

<name_or_company>Name of person or company</

name_or_company>

<information>

[Provide the gathered information here in a clear, concise

manner]

</information>

Remember to use only the information you can find

through the LinkedIn tool. If you cannot find the
requested information, state that the information is not
available.

// End Prompt

As you can see, it is like the sub-agent prompts. We are

telling the manager that it needs to look up the email subject
to invoke the appropriate sub-agent. Then, within the steps for
a person or company, invoke the respective sub-agent. Finally,
format and send the info extracted to the sender.
That’s it! We will test the multi-agent system now.

4.5. Run and Test

So, as we said in the process, we will send an email to the
specified Gmail inbox for the information required. We just need
to specify what type of info we need in the subject, e.g., “person

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info,” and then, in the body, specify the person or company for
which we need information. Here are the steps:

1. Send Test Email: Send a test email with subject lines

saying «person info» and the name of the person you
need information for in the email body.

2. Monitor Agent: This email will trigger the manager agent

to start working on the request. Go to the dashboard and
click on “sales info agent.” You will find the run details
for the agent, as presented below:

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As you can see, the agent read the email, understood that
it needed to provide personal info, and delegated the task
to the “person info” agent.

3. Sub Agent Delegation: If you click on the “view

conversation” button, you will see that the person
info agent used the Google search tool and the Extract
LinkedIn profile tool to extract the profile information.

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4. In the Sales Agent info run details (step 2 above), you

will see that it ultimately sent an email back to the sender
with the information. Here is the email reply I got.

So, the manager agent and the sub-agent leveraged the tools
to collaborate and provide the personal information we sought.

Conclusion: Your First AI Agent

Congratulations! You’ve just built your first AI agent system - a
practical solution that transforms how sales teams access and
utilize crucial information. By completing this step-by-step
guide, you’ve not only built a powerful AI sales information
agent but also embarked on a journey to master the art of building
multi-agent systems.
Through this hands-on guide, we’ve walked through the four
essential steps of agent building:
1. We identified a valuable opportunity where AI agents
could make a real difference in sales operations
2. We designed a thoughtful multi-agent system where
specialized agents collaborate seamlessly to achieve the tasks
3. We mapped out and implemented clear processes that
orchestrate how the agents communicate, delegate tasks, and
deliver results
4. We implemented the solution using Relevance AI’s no-
code platform
But this is just the beginning. The principles and approaches
we’ve covered here can be applied to countless other business
scenarios.

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Remember that building effective AI agents is an iterative

process. As you deploy and use your agent, you’ll discover
new ways to enhance its capabilities, improve its responses,
and expand its functionality. Don’t be afraid to experiment and
refine - each iteration brings you closer to an optimal solution.
We encourage you to take these concepts and tools and apply
them to your unique challenges.

CHAPTER 12 – Use Cases: Enterprise AI

Agent Application
This appendix presents 15 successfully implemented AI agent
applications across key industries. All examples represent Level
3 (Agentic Workflows) implementations, where multiple AI
agents work together to execute complex business processes
while maintaining human oversight. A critical success factor in
launching an agentic AI transformation is identifying, assessing,
and prioritizing the right business use cases. To accelerate this
process, we’ve curated these examples from our extensive
implementation experience across industries. Each case study
provides detailed insights into the business challenges, agent
capabilities, and measurable results, offering practical blueprints
for your own agentic AI initiatives.

1. OPERATIONS & SUPPLY CHAIN

Supplier Communications
Business Challenge A major airline services organization faced
increasing operational complexity, managing thousands of
supplier communications and document processing tasks daily.
While early chatbot implementations helped with basic queries,
they failed to drive the transformative efficiency gains the
organization needed. The company recognized that automation
alone was not enough—true operational improvements required

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an intelligent system capable of independently coordinating

tasks, executing workflows, and adapting to dynamic business
conditions.

Agent Capabilities
• Interpret incoming supplier messages using natural
language processing
• Route communications based on urgency, contract terms,
and operational impact
• Generate context-aware responses aligned with supplier
agreements
• Execute follow-up actions including approvals and
system updates
• Learn and refine decision-making based on interaction
patterns
• Monitor and flag anomalies requiring human intervention
• Maintain compliance with aviation industry standards

Impact and Results The system follows a “management by

exception” model borrowed from aviation operations - routine
communications are fully automated while human operators
engage only for anomalies or high-risk scenarios. The early
impact of this approach has been significant. Processing times
have decreased substantially, while accuracy has improved
through the system’s consistent application of business rules.
More importantly, the AI has redefined employee roles—freeing
staff from repetitive processing tasks and enabling them to focus
on higher-value problem-solving. The company sees this shift
as critical for addressing workforce challenges, particularly in
regions facing labor shortages.

Manufacturing Operations Coordination

Business Challenge A global manufacturer struggled with
coordinating complex facility operations across multiple

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production lines, suppliers, and maintenance schedules.

Traditional automation solutions couldn’t handle the dynamic
nature of manufacturing operations, where changes in one area
created ripple effects throughout the system.

Agent Capabilities
• Monitor real-time production metrics and equipment
status
• Coordinate scheduling across multiple production lines
• Manage inventory levels and supplier relationships
• Optimize maintenance timing based on production
demands
• Adjust staffing requirements based on production
changes
• Generate automated reports and alerts for stakeholders
• Predict and prevent potential bottlenecks

Impact and Results The agent system demonstrated its value

during a major supply chain disruption, where it autonomously
recalculated production schedules, identified alternative
suppliers, adjusted staffing requirements, and modified
maintenance schedules to optimize available resources. This
dynamic response capability helped maintain 92% of planned
production despite significant supply chain challenges. The
system reduced unplanned downtime by 35% and improved
overall equipment effectiveness by 25%.

Supply Chain Risk Management

Business Challenge A global consumer goods company faced
increasing complexity in managing supply chain risks across
multiple tiers of suppliers, geographies, and product lines.
Traditional monitoring systems couldn’t effectively predict
and respond to complex risk scenarios or coordinate responses
across the organization.

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Agent Capabilities
• Monitor global supply chain events and disruptions in
real-time
• Assess the impact of disruptions on multiple supply
chain tiers
• Identify alternative sourcing options and calculate costs
• Coordinate response plans across multiple departments
• Generate risk mitigation recommendations
• Track supplier performance and compliance
• Maintain continuous communication with stakeholders

Impact and Results During a major global supply chain

disruption, the system automatically identified affected suppliers,
calculated impact across product lines, and coordinated
alternative sourcing strategies. The agent network reduced
response time to supply chain disruptions by 60% and decreased
supply chain risk incidents by 40%. Most importantly, it enabled
proactive risk management, with 85% of potential disruptions
addressed before impacting operations.

2. SALES & REVENUE MANAGEMENT

Complex B2B Sales Orchestration
Business Challenge A technology company faced increasing
complexity in managing enterprise sales cycles involving
multiple stakeholders, lengthy approval processes, and
complex solution configurations. Traditional CRM systems
couldn’t effectively coordinate the numerous touchpoints and
dependencies in complex B2B sales processes.

Agent Capabilities
• Analyze historical deal patterns to identify success
factors
• Coordinate follow-up activities across sales teams

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• Generate personalized proposal documents

• Track competitive intelligence and market dynamics
• Manage pipeline and forecast updates
• Optimize territory and account assignments
• Automate routine sales documentation

Impact and Results The agent system transformed the

sales process by identifying patterns in successful deals and
automatically adjusting engagement strategies. It reduced the
time sales teams spent on administrative tasks by 40% while
increasing win rates by 28%. The system’s ability to coordinate
complex stakeholder communications and automate proposal
generation reduced sales cycle time by 40%, allowing sales
teams to focus on relationship building and strategic discussions.

Account Growth & Retention Management

Business Challenge A software-as-a-service company struggled
to proactively identify growth opportunities and churn risks
across their customer base. The traditional account management
approach relied heavily on manual monitoring and individual
account manager insights, making it difficult to scale effectively
and maintain consistent service levels.

Agent Capabilities
• Monitor customer usage patterns and engagement
metrics
• Identify expansion opportunities based on usage trends
• Detect early warning signs of potential churn
• Coordinate proactive outreach and engagement activities
• Generate personalized growth recommendations
• Automate routine account management tasks
• Track customer health scores and success metrics

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Impact and Results The agent system revolutionized account

management by providing early insights and coordinating
proactive interventions. It identified at-risk accounts an average
of 60 days earlier than previous methods and increased successful
retention interventions by 45%. The system also drove growth,
with a 35% increase in expansion revenue through better-timed
and more relevant upsell opportunities. Customer satisfaction
scores improved by 25% due to more proactive and personalized
engagement.

3. CUSTOMER EXPERIENCE & SERVICE

Healthcare Access Navigation
Business Challenge A major healthcare system identified that
underserved populations were struggling to access available
healthcare services and assistance programs. The traditional
process required patients to navigate multiple complex systems,
fill out numerous applications, and coordinate across various
agencies - creating significant barriers to care. Many eligible
patients were missing out on critical services simply due to the
complexity of the application processes.

Agent Capabilities
• Conduct conversational interviews to understand patient
situations
• Autonomously gather documentation from authorized
sources
• Identify suitable programs and services based on patient
circumstances
• Complete and submit applications across multiple
assistance programs
• Monitor application statuses and respond to information
requests
• Coordinate practical support services like transportation

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• Maintain clear communication with patients through

preferred channels

Impact and Results The AI agent system transformed access

to care by acting as an intelligent navigator and advocate. When
a patient mentions losing their job, the system automatically
evaluates eligibility across multiple assistance programs, initiates
applications, and coordinates support services - all while keeping
the patient informed through their preferred communication
channel. Processing times decreased from weeks to days, while
program enrollment rates increased significantly.

Banking Service Coordination

Business Challenge A major retail bank struggled with
coordinating complex customer service requests across multiple
channels and product lines. Traditional banking systems operated
in silos, making it difficult to provide seamless service, especially
for requests involving multiple departments or products.

Agent Capabilities
• Process and route customer inquiries across channels
• Coordinate responses across multiple departments
• Handle complex transaction investigations
• Manage fraud alerts and security measures
• Automate routine service requests
• Maintain compliance with banking regulations
• Generate personalized customer communications

Impact and Results The agent system dramatically

improved service delivery by coordinating across previously
siloed departments. Mortgage processing time was reduced
from 45 days to 18 days, suspicious activity response time cut
by 80%, and customer satisfaction scores increased by 35%. The
system’s ability to handle complex multi-department requests

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reduced resolution times by 60% while improving accuracy and

compliance.

Insurance Claims Processing

Business Challenge A large insurance provider faced increasing
complexity in managing claims involving multiple parties,
service providers, and documentation requirements. Traditional
claims processing systems couldn’t effectively coordinate the
various stakeholders or adapt to changing circumstances during
claims resolution.

Agent Capabilities
• Analyze claims documentation and photos using
computer vision
• Cross-reference policy details and coverage limitations
• Coordinate with multiple service providers
• Manage communication between adjusters and claimants
• Identify potential fraud patterns through network analysis
• Optimize settlement timing based on multiple factors
• Maintain regulatory compliance throughout the process

Impact and Results The agent system demonstrated its

value, particularly in complex multi-party claims. In one case, it
coordinated communications between five insurance companies,
three repair shops, and multiple medical providers, maintaining
clear documentation and speeding resolution by 60%. Overall
claims processing time reduced by 40%, accuracy improved by
35%, and customer satisfaction scores increased by 30%. The
system’s fraud detection capabilities led to a 25% increase in
identifying suspicious claims patterns.

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4. RISK, COMPLIANCE & SECURITY

Financial Fraud Detection
Business Challenge A global financial institution struggled
with detecting increasingly sophisticated fraud patterns across
multiple channels and transaction types. Traditional rule-based
fraud detection systems were too rigid to adapt to evolving
fraud schemes and generated high rates of false positives that
consumed investigator time.

Agent Capabilities
• Monitor transaction patterns across all channels in real-
time
• Correlate data points across multiple accounts and
systems
• Identify complex fraud patterns using network analysis
• Coordinate immediate response actions across
departments
• Generate investigation packages for complex cases
• Update fraud detection rules based on new patterns
• Maintain audit trails of all detection and response actions

Impact and Results The agent system revolutionized fraud

detection by identifying subtle patterns that traditional systems
missed. During one incident, it detected a coordinated fraud
attempt by recognizing subtle patterns across multiple accounts
and channels, preventing potential losses before any transactions
were completed. The system reduced false positives by 60%,
increased fraud detection rates by 35%, and cut response times
from hours to minutes. Annual fraud losses decreased by 45%
while investigator efficiency improved by 40%.

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 APPENDICES: Practical Resources

Regulatory Documentation
Business Challenge A global life sciences company faced
mounting challenges in managing complex documentation
across research, development, and manufacturing. The highly
regulated nature of the industry required a sophisticated system
capable of understanding GxP guidelines, manufacturing
deviations, and compliance intricacies. Traditional document
management systems couldn’t handle the depth of regulatory
interpretation needed.

Agent Capabilities
• Interpret GxP requirements and identify relevant policies
• Search internal databases and external regulatory sources
• Assess manufacturing deviations and cross-reference
past incidents
• Compile structured, audit-ready reports
• Maintain full documentation traceability
• Coordinate compliance workflows across departments
• Alert stakeholders of potential compliance issues

Impact and Results The system transformed regulatory

documentation management by providing intelligent
interpretation and coordination. For example, if a quality
assurance specialist investigates a manufacturing deviation,
the system doesn’t just provide raw data. It autonomously
identifies relevant historical cases from similar deviations,
cross-references regulatory updates that may impact resolution
protocols, synthesizes a compliance report outlining risks,
recommended actions, and supporting documentation, and alerts
key stakeholders if patterns indicate systemic issues requiring
process adjustments. Early results have demonstrated a
fundamental shift in how regulatory documentation is managed.

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IT Security Operations
Business Challenge A technology company struggled with
coordinating security responses across their complex IT
infrastructure. Traditional security tools generated numerous
alerts but couldn’t effectively prioritize threats or coordinate
comprehensive responses across multiple systems.

Agent Capabilities
• Monitor system activities across all platforms
• Correlate security events to identify threat patterns
• Coordinate immediate response actions
• Update security rules across systems
• Initiate vulnerability assessments
• Generate security incident reports
• Maintain compliance with security standards

Impact and Results The agent system demonstrated its value

during a potential zero-day threat incident, where it identified
and contained the threat before it could impact critical systems.
Overall, incident response times reduced by 60%, false positive
alerts decreased by 75%, and system availability improved
by 45%. The system’s ability to learn from past incidents and
automatically update security rules led to a 40% reduction in
successful breach attempts.

5. KNOWLEDGE WORK & ANALYTICS

Competitive Intelligence
Business Challenge A financial services organization faced
increasing challenges in monitoring competitor financial
performance, reconciling market data with internal benchmarks,
and generating timely insights for executive decision-making.
Traditional processes relied on manual data collection from
earnings reports, industry publications, and financial filings,

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followed by extensive reconciliation efforts to align external

figures with internal forecasting models. This approach was
time-consuming, inconsistent, and limited in its ability to
provide real-time intelligence.

Agent Capabilities
• Continuously scrape and process financial reports and
earnings statements
• Cross-reference external figures against internal models
• Normalize data across different reporting methodologies
• Generate structured competitive analysis reports
• Identify emerging market trends and risks
• Provide real-time responses to executive queries
• Maintain historical analysis and trend data

Impact and Results The agent system transformed

competitive intelligence gathering by providing real-time,
contextual insights. Unlike traditional automation, this system
does more than aggregate financial data—it actively interprets
and contextualizes information, identifying anomalies, strategic
shifts, and potential risks before they become evident in
standard reporting cycles. Leadership teams can interact with
the system through a conversational interface, requesting up-to-
date financial comparisons, competitive positioning analyses, or
scenario-based forecasts without relying on manual intervention.
The system’s ability to autonomously refine its outputs over time
has significantly reduced the burden on analysts while improving
the accuracy and speed of strategic insights.

Market Research & Synthesis

Business Challenge A consulting firm faced increasing difficulty
in gathering, analyzing, and synthesizing market research
across multiple industries and data sources. Traditional research

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methods were time-consuming and often missed important

connections across different market segments and trends.

Agent Capabilities
• Gather data from multiple public and proprietary sources
• Analyze market trends across industries
• Identify emerging opportunities and threats
• Generate comprehensive market reports
• Maintain up-to-date industry knowledge bases
• Coordinate research workflows across teams
• Create customized research briefings

Impact and Results The agent system revolutionized the

firm’s research capabilities by providing deeper, more connected
insights in less time. Research that previously took months is
now completed in weeks, with a 40% increase in the depth
of insights generated. The system’s ability to identify cross-
industry patterns has led to several breakthrough client insights,
resulting in a 35% increase in consulting engagements and a
45% improvement in client satisfaction scores.

6. EMPLOYEE & ADMINISTRATIVE SERVICES

HR Operations
Business Challenge A global corporation struggled with
coordinating complex HR processes across multiple regions,
time zones, and regulatory environments. Traditional HR
systems operated in silos, making it difficult to provide
consistent employee experiences and maintain compliance
across jurisdictions.

Agent Capabilities
• Coordinate end-to-end hiring processes
• Analyze market salary data and skill requirements

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 APPENDICES: Practical Resources

• Craft effective job descriptions based on past success

• Screen applications using sophisticated pattern matching
• Manage complex interview scheduling across time zones
• Orchestrate personalized onboarding journeys
• Monitor workforce patterns for retention risks
• Identify emerging skill gaps and development needs
• Ensure compliance across multiple jurisdictions

Impact and Results The agent system transformed HR

operations by coordinating previously fragmented processes.
Hiring processes accelerated by 45%, candidate quality improved
by 30%, and early-stage turnover reduced by 25%. The system’s
ability to predict retention risks and identify skill development
needs led to a 40% improvement in employee retention. HR
teams report spending 60% more time on strategic initiatives
rather than administrative tasks.

IT Service Management
Business Challenge A multinational company struggled with
managing IT service requests across their global infrastructure.
Traditional IT service management tools couldn’t effectively
prioritize requests, coordinate responses across technical teams,
or maintain consistent service levels across regions.

Agent Capabilities
• Analyze and route service requests based on context
• Coordinate responses across multiple technical teams
• Monitor system performance metrics
• Orchestrate software deployments and updates
• Manage access requests and security protocols
• Generate performance and compliance reports
• Learn from past incidents to improve response times
• Maintain service level agreements across regions

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Impact and Results The agent system revolutionized IT

service management through intelligent coordination and
proactive problem-solving. During a recent cloud infrastructure
incident, the system coordinated database failover, network
rerouting, and application scaling while keeping stakeholders
informed in real-time. Overall incident resolution times
reduced by 60%, routine ticket volume decreased by 40%,
and system availability improved by 45%. Most significantly,
IT teams shifted from reactive firefighting to proactive system
enhancement, leading to a 35% reduction in major incidents.

***

These enterprise applications demonstrate the transformative

potential of agentic AI across diverse business functions. While
each implementation is unique, common patterns emerge:
significant efficiency gains, improved accuracy, and perhaps
most importantly, the elevation of human work to more strategic
activities. We encourage organizations to use these cases as
starting points for their own transformations. The opportunities
for implementing agentic AI exist across all functions and
industries - the key is starting with well-defined use cases that
can demonstrate clear value while building organizational
capabilities and confidence.

CHAPTER 12 – Use Cases: Personal

Productivity AI Agent Applications
While enterprise applications demonstrate organizational
impact, personal productivity applications often provide the
most immediate and tangible benefits of agentic AI. These
five implementations showcase how AI agents can transform
individual work patterns, providing practical starting points
for organizations looking to build momentum for broader

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adoption. Each application represents proven implementations

that maintain human agency while significantly enhancing
productivity.

1. Email Management & Communication

Business Challenge Professionals were spending 15-20 hours
weekly managing email communications, struggling with inbox
overload, response prioritization, and maintaining consistent
communication quality across different stakeholders.

Agent Capabilities
• Analyze incoming emails for urgency and importance
• Draft contextual responses based on previous
communications
• Identify action items and follow-up requirements
• Coordinate responses across multiple threads
• Generate consistent communication templates
• Maintain appropriate tone across different stakeholders
• Schedule follow-ups and track pending responses

Impact and Results A marketing director reported saving 15

hours weekly using the system, noting, “It’s like having a personal
assistant who knows exactly how I think and work.” The system
reduced email response time by 60% while improving response
quality and consistency. Teams using the system reported 40%
more time available for strategic work.

2. Calendar & Meeting Optimization

Business Challenge Professionals struggled with managing
complex scheduling demands, protecting focused work time,
and ensuring productive use of meeting hours. Traditional
calendar tools couldn’t effectively balance competing priorities
or maintain work-life boundaries.

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Agent Capabilities
• Understand and protect work priorities
• Coordinate scheduling across time zones
• Protect designated deep work sessions
• Optimize meeting distributions
• Generate meeting preparations and summaries
• Track follow-up items and commitments
• Maintain work-life balance boundaries

Impact and Results The system reduced administrative

scheduling time by 70% while improving meeting effectiveness.
A senior executive reported that “it’s like having a strategic
assistant who understands not just my schedule, but my priorities
and work style.” Teams using the system reported 35% more
time for focused work and a 40% reduction in meeting overload.

3. Research & Information Synthesis

Business Challenge Knowledge workers spent excessive
time gathering, analyzing, and synthesizing information from
multiple sources. Traditional research tools couldn’t effectively
connect insights across different domains or generate structured
outputs tailored to specific needs.

Agent Capabilities
• Gather information from multiple authorized sources
• Analyze and cross-reference data points
• Generate structured research summaries
• Identify key trends and patterns
• Create customized report formats
• Maintain source documentation
• Track research progress and updates
• Suggest relevant additional sources

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 APPENDICES: Practical Resources

Impact and Results The agent system transformed research

efficiency by automating information gathering and synthesis. A
consultant noted that this capability “transformed my ability to
leverage our firm’s collective knowledge, making each project
start from a much stronger foundation.” Research time reduced
by 60% while the depth and quality of insights improved by
40%.

4. Task & Project Coordination

Business Challenge Professionals struggled with managing
multiple projects, coordinating dependencies, and maintaining
visibility across various workstreams. Traditional project
management tools couldn’t effectively adapt to changing
priorities or coordinate across different tools and teams.

Agent Capabilities
• Break down complex projects into manageable tasks
• Coordinate dependencies across multiple workstreams
• Monitor deadlines and progress
• Adjust schedules based on priority changes
• Generate status updates and reports
• Identify potential bottlenecks
• Maintain project documentation
• Track resource allocation

Impact and Results The system transformed project

management effectiveness through intelligent coordination. One
product manager highlighted how “it kept track of hundreds
of small details I would have missed, allowing me to focus
on strategic decisions.” Teams using the system showed 40%
higher project completion rates and reported 35% more time for
strategic thinking.

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5. Document Creation & Review

Business Challenge Professionals spent significant time
creating, reviewing, and revising documents, often struggling
with maintaining consistency, ensuring accuracy, and managing
version control across multiple stakeholders.

Agent Capabilities
• Generate initial document drafts
• Review for consistency and accuracy
• Track changes and versions
• Coordinate review workflows
• Maintain style guidelines
• Check citations and references
• Generate executive summaries
• Format documents to standards

Impact and Results The agent system revolutionized

document management workflows. Document creation time
reduced by 50%, while review cycles shortened by 40%. Teams
reported improved document quality and consistency, with one
manager noting, “It’s like having a dedicated editor who knows
our style guide perfectly.” Most importantly, professionals
reported having more time for high-value content creation rather
than formatting and administrative tasks.
These personal productivity applications demonstrate how
AI agents can transform individual work patterns, freeing up
time for more strategic and creative tasks while improving output
quality and consistency. The key impact across all applications
has been not just efficiency gains, but a fundamental shift in how
professionals can focus their time and energy on truly value-
adding activities.

***

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These personal productivity applications offer accessible entry

points for organizations beginning their agentic AI journey.
By starting with these individual-level implementations,
organizations can build familiarity and confidence with AI agents
while delivering immediate value to employees. The results
consistently show not just efficiency gains, but a fundamental
shift in how professionals can focus their time and energy on
truly value-adding activities. We encourage organizations to
consider these applications as initial steps toward broader
agentic AI transformation, using the demonstrated successes to
build momentum for more comprehensive implementations.

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 Index
A F
Agent-as-a-Service 329, 330 fallback 147, 148, 150, 151, 314,
Agent Capabilities 373, 531, 532, 315
533, 534, 535, 536, 537, 538,
539, 540, 541, 542, 543, 545, G
546, 547, 548 Generative AI 1, 8, 18, 44, 45, 311,
Agentic Opportunities 256, 258, 415, 417, 418, 419, 420, 499
262, 318
Agent-to-Agent Economy 28, 336 H
AI Accountability 307
AI Agent Progression Framework Human-AI Collaboration 282
502
AI Ethics 397, 398 I
AI Governance 309, 482 Intelligent Automation xiv, 40, 73,
Attention Mechanism 209 271, 273, 274, 410, 493, 494,
Autonomous Agents 22, 71, 299 500
C L
Change Management 357, 371, 384 Large Language Models (LLMs)
Circuit breaker 304 169
Cognitive Diversity 188 Long-Term Memory 204, 216, 217,
Collective Intelligence 187, 481 218, 219, 221, 249
Computer Use AI 115
Context Window 207, 208 M
D Memory Consolidation 225
Meta-Learning 161, 241
Digital Worker 47, 84 Multi-Agent System 90, 96, 99,
133, 185, 186, 190, 191
E
Episodic Memory 219, 221, 223, P
227 Procedural Memory 220, 221, 223
Error Handling 136, 303, 508

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 Agentic Artificial Intelligence

R T
Reasoning Capabilities 185, 186 Three Keystones 26, 124, 127, 128
Reasoning Models 169 Tool Resilience Framework 148,
150
S Training 169, 170, 333, 366, 367
Scaling AI Agents 29, 403 Transparency in AI 309
Semantic Memory 220, 221, 223 U
Short-Term Memory 204, 206, 212,
214, 249 Universal Basic Income 30, 469
SPAR Framework 62, 64

552