While business and technology leaders are working to reap value from AI today, the future is barreling toward us, and it’s vital to build a foundation for what’s quickly coming. Tech leaders are betting that the future of AI is agentic. In other words, organizations will adopt intelligent systems that not only perform tasks autonomously, but also make decisions with near-human-like precision, from writing code to handling ecommerce operations, functioning as automated sales agents and more.
Agentic AI was the focus of the VentureBeat AI Impact Tour: “Agentic AI — the next giant leap forward in the AI revolution,” presented by Outshift by Cisco. Speakers from Outshift, Meta, Asana and Intuit joined VB CEO Matt Marshall to explore how orgs can plan for an agentic AI future and other onrushing advances in AI.
Building out an agentic future
“If you think about that future where these agentic systems are going to work together to solve bigger problems, we need distributed agentic systems computing and we need an open, interoperable internet of agents,” Vijoy Pandey, GM and SVP, Outshift by Cisco told Marshall. “Innovation slows down when you’re in a walled garden. Whether you’re an infrastructure vendor, operator, an app developer and most importantly a consumer or customer, an open system provides value for each individual link in the chain.”
“Innovation slows down when you’re in a walled garden.”
Agentic systems that learn how talk to each other and interconnect have the power to change the way humans work, starting with software and IT and then moving toward knowledge work, services and even physical work as robotics evolve. They’ll also need to be integrated into existing software systems and physical environments, as well as instantiated on those existing software systems, whether it’s cloud, on-prem or embedded in a robotic solution.
Tying it all together requires abstraction layers. That will look like open models, open tooling, an orchestration and discovery layer and then a communication layer that is secure, stable and open. Then there’s handling probabilistic outcomes, communication through NLP, the exchange of state information and more.
“These could be pretty massive problems to solve,” Pandey said. “We’re looking for these problems and what they look like and how to solve them. That’s where the future is.”
The time to start on AI agents is now
Today’s big question is whether the technology is mature enough to realize its full potential — and it isn’t quite yet. However, that can’t be a barrier, Mano Paluri, VP of generative AI engineering at Meta, said, during a one-on-one conversation with Marshall.
“You can’t wait. Agents clearly feel like the next step in the evolution of these models.”
“You can’t wait,” he added. “In that sense, I would say that it’s ready. Agents clearly feel like the next step in the evolution of these models. The way we have been thinking about it is moving away from a model to a system that has multiple components that are customizable.”
In the hunt for autonomous systems that can foresee, learn, reason, act and iterate to solve a complex problem, we’ve already come far in perception — foundational models are able to learn from text and images. We’re still in the early stages of reasoning through complex problems, but today models can learn at far larger scales than ever before over the last decade. And these models are beginning to plan, from both an inner and an outer loop perspective. Today the outer loop is the human training the model. Next will be the agent handling parameters itself.
The Meta AI agent
Today’s Meta AI agent is the first step in the evolution of LLMs as Meta moves away from a model to a system that has multiple, customizable components. The goal is to fine-tune the model for every use case, extend the context window, adapt to a new language and so on, for all four billion customers.
“We also believe in a family of agents,” he said. “This incarnation of Meta AI is a user assistant, but we also think everyone should be able to customize the agent in the way that they want. This is the family of agents where businesses can create a billing agent. Creators can have their own agent to reach a larger scale. Advertisers can have creation capabilities that are unprecedented.”
Agentic AI use cases: challenges and opportunities
To close out the night, Paige Costello, head of AI at Asana, Shubha Pant, VP, AI/ML at Outshift by Cisco, Kumar Sricharan, VP of technology and chief architect for AI at Intuit, joined Marshall for a conversation about the use cases agentic AI will open up, and the challenges and opportunities that will come hand in hand.
Real-world case studies
Handling requests within a workflow can be a huge time suck, but that’s where agentic AI comes in. Asana has embedded agentic AI for both chat and workflow use cases. In the case of workflows, it can handle a request at the outset, determining where to prioritize it, whether there’s enough information to get started and who should be included. It’s a great place for a company to start adding agentic workflows, Costello added.
“The agentic piece is, how much decision-making or autonomy does it have to do these things in the context of those workflows?”
“There are so many opportunities where AI can be a partner in doing this work,” Costello said. “The agentic piece is, how much decision-making or autonomy does it have to do these things in the context of those workflows? We’ve seen great success with security companies, with marketing agencies. There are many other use cases where we’re starting to see things like creative requests, working through revisions and feedback and approval loops.”
At Intuit, they’re automating across their entire suite of financial products, and offering insights and financial guidance. Across that ecosystem of tasks, they’re experimenting with AI, especially in areas where building hand-engineered solutions would be time-consuming, or even just fizzle. For instance, small businesses have a broad array of characteristics and needs. During onboarding, the customer is required to detail all that information so that Intuit can classify them.
“These agents essentially autonomously operate on top of that information and help the customer onboard with minimal effort on their part.”
“Now, with the rise of agentic AI, we’re finding that we can use these systems, these different agents that can work in unison to allow the customer to give us access to the different sources of their information,” Sricharan said. “Then these agents essentially autonomously operate on top of that information and help the customer onboard with minimal effort on their part.”
Internally, agentic AI is helping the company navigate tax code changes that impact products. Instead of a team of developers researching and implementing new elements, they can use agentic AI there to span a variety of functions, all the way from detecting where the changes are, to making associations with our code and determining what changes need to be then made, acting as a copilot for developers.
“The goal is to predict IT issues before they arise, identify root causes when issues occur and offer mitigation strategies until full resolution.”
Outshift has an incubation team focused on building a multi-agent predictive diagnostic and remediation tool for enterprises across their tech stacks. The goal is to predict IT issues before they arise, identify root causes when issues occur and offer mitigation strategies until full resolution. There are other agentic AI projects in progress including architecture development, open standards for orchestration of agents, composition of multi agent systems, and an open agent protocol for inter-agent communication.
“The main challenges for agentic AI right now are threefold,” Pandey said. “First, how AI agents discover each other and understand each others’ capabilities. Second, how they collaborate to solve problems and handle uncertain outcomes. And third, how they communicate using imprecise natural language instead of fixed structures like traditional APIs.”
“We need to figure out how to create standards and open-source guidelines for these AI systems that deal with probabilities,” he added. “It’s time for the tech community to join forces and build these solutions together.”
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