Chirag Agrawal
Designing Agentic Systems that Ship
Abstract:
Agentic AI systems are closed loop programs that plan, invoke tools, evaluate results, and iterate until a goal is met, yet there is great deal of depth involved in running them sustainably in production. This session presents a practical framework for designing that loop for production. We focus on the ReAct as the foundation for tool-based agents and multi-agent collaboration, disciplined tool selection and reliable routing, and the operational realities of deploying agents at scale. We address context rot in multi-turn conversations and share robust context management practices that keep agents on track. We also cover cost control in multi-turn systems and techniques that improve perceived latency, including prompt caching and context compression. Attendees leave with a concise checklist, actionable patterns, and guardrails they can apply immediately to ship resilient agentic features.
Profile:
Chirag Agrawal is a seasoned technology professional with over a decade of experience in building large scale AI platforms, distributed systems and developer tooling. As a Senior Engineer and Tech Lead at Amazon, he specializes in LLM infrastructure, advanced AI-powered conversational systems and multi-agent orchestration including agent execution, system prompt engineering, AI tool use, AI memory, Software Development Kit and Compilers. Chirag leads cross-functional architectural initiatives, driving lower latency, reliability, and scale for Alexa and its developer ecosystem.