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Tue, April 21DeepTech OpsTech Architecture
AI agents do not fail because they cannot reason. They fail because the cost of their behaviour is not designed upfront. What begins as a simple agentic workflow with a planner, tools, and retrieval quickly grows into a system of chained calls, recursive loops, repeated memory access, and cascading model invocations. Each step may appear inexpensive, but at scale the combined cost becomes significant. For enterprise systems, this unpredictability is a critical risk.
This session examines the unit economics of agentic AI systems in production. Drawing from real deployments, it identifies where costs accumulate, including orchestration overhead from uncontrolled reasoning loops, excessive tool calls due to poorly defined boundaries, context window growth from unmanaged memory, and escalation from multi-agent coordination. The session also presents practical approaches to address these challenges, including agent loop budgeting, tiered model routing for sub-tasks, caching strategies for tool outputs, and observability patterns that make spend predictable.
What You Will Learn
Who Should Attend
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Akhil Jain is a Senior Solutions Architect at Amazon Web Services with 17+ years of experience across the USA and India, building and operationalizing AI systems at enterprise scale. He specializes in GenAI architecture, agentic workflows, LLM orchestration, IoT and Big Data — working hands-on with engineering teams to move AI from promising prototype to production-grade reality, within the real constraints of cost, latency, and data maturity.
Having worked directly with AWS customers across the USA, Canada, Mexico, El Salvador, the Netherlands, Australia, and India, Akhil brings a genuinely global perspective to enterprise technology — one shaped by firsthand experience of how AI adoption, cloud strategy and architectural decisions play out across different markets, industries and regulatory environments.
Before AWS, he led big data and cloud architecture programs at Informatica. A Carnegie Mellon alumnus, he believes GenAI's true potential lies not in benchmarks or demos — but in the lasting human impact it creates when built with purpose and deployed at scale.
His sessions are built on production reality — the tradeoffs, the failure modes and the hard numbers that practitioners actually encounter when scaling AI beyond the demo.
