Swami Sivasubramanian’s Post

Your observation about the shift from exploration to practical implementation really resonates. The transition from proof-of-concept to solving actual business challenges is fascinating. What specific types of business problems are you seeing customers prioritize first when implementing agentic AI solutions, and are there any surprising use cases emerging?

Agentic AI delivers real value when it’s tied to core business workflows, not hype. The shift to autonomous decision-making is where companies will see true ROI. Strong focus on integration and measurable outcomes.

Great insights on how agentic AI is creating real business value. We’ve been exploring AgentCore as part of our work on agentic AI — an LLM-neutral framework with short- and long-term memory capabilities. Looking forward to seeing how this space evolves at re:Invent and beyond.

This shift is critical. Agentic AI moves from proofs-of-concept to production—delivering autonomous value. The focus on business problems first is where real adoption happens.

Swami Sivasubramanian 𝑊ℎ𝑎𝑡 𝑓𝑟𝑎𝑚𝑒𝑤𝑜𝑟𝑘 𝑑𝑜 𝑦𝑜𝑢 𝑝𝑟𝑜𝑝𝑜𝑠𝑒 𝑓𝑜𝑟 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑦𝑖𝑛𝑔 𝑡ℎ𝑒 𝑅𝑂𝐼 𝑜𝑓 𝑎𝑔𝑒𝑛𝑡𝑖𝑐 𝐴𝐼 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑡𝑜 𝑡𝑟𝑎𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝐿𝐿𝑀-𝑏𝑎𝑠𝑒𝑑 𝑎𝑠𝑠𝑖𝑠𝑡𝑎𝑛𝑡𝑠? Which 3–5 KPIs are mission-critical for executive stakeholders beyond generic “time/cost savings”?

AWS is clearly positioning agentic AI as the new execution layer of the enterprise. Kiro, Quick Suite, and AgentCore show how fast we’re moving from demos to real autonomy, and the infrastructure behind it is just as important as the models. Exciting to see how this all comes together at re:Invent.

The shift from experimentation to real business impact is exactly what makes this moment so exciting. Agentic AI is moving from “what’s possible” to “what’s working,” and 2025 is going to redefine that gap even further.

The shift from experimentation to real business impact is exactly what makes this moment so exciting. Agentic AI is moving from 'what's possible' to 'what's working,' and 2025 is going to redefine that gap even further!

Agentic AI only creates value when you can prove it. The gap is not more models; it is visibility. You need workflow-level KPIs for agents: productivity gains (Hrs and $) / costs / ROI and depending on the actualy business objective additional KPIs (for example, accuracy and QA rates, policy hits, and time-to-value ETC ETC). Without that, you end up in “innovation theater.” Olakai delivers them: unified signals on adoption, cost, ROI, and compliance in one place so leaders double down on what works and retire what does not. A living AI scorecard that turns pilots into an operating plan.

Quick Suite looks to be a needed answer to many organisations questions around working across their apps and data and the $20/month per user brings it squarely into the budget range that we see for tools like Gemini, ChatGPT and below Copilot 365. We eill be exploring this, looks cool Swami