In this episode of The Digital Executive, host Brian Thomas welcomes Chirag Agrawal, a senior technologist and AI infrastructure expert with deep experience building large-scale platforms, distributed systems, and multi-agent orchestration for Alexa. With over a decade in advanced AI systems, Chirag shares his perspective on what it really takes to move from “having a model” to running resilient, scalable AI agents in production.

Chirag explains why models should be treated as dependencies—not the product itself—and why teams repeatedly fall into the trap of building agents from scratch instead of relying on proven frameworks. He unpacks the often-overlooked complexity behind agent systems: retrieval, tool orchestration, memory, context compression, caching, evaluation frameworks, and guardrails that must be treated as first-class components.

The conversation dives into the balance between developer freedom and architectural discipline, highlighting how strong developer tooling actually accelerates experimentation while enforcing performance, safety, and reliability across teams.

Chirag also details the key operational metrics that matter most in production agent platforms—from latency breakdowns to token usage patterns to offline quality metrics—and discusses the art of navigating trade-offs across quality, cost, and speed.

Looking ahead, Chirag emphasizes that ethics, bias mitigation, auditability, and transparency must be embedded at the foundational layer of AI platforms. He highlights the importance of interoperability standards such as MCP and A2A, predicting a future where agents discover, authenticate, and collaborate across systems—much like the evolution from early mobile apps to fully mature ecosystems.

He paints a future shaped by an “internet of agents”: interconnected, multi-agent systems that share capabilities while maintaining their own governance boundaries—a transformative step toward next-generation production AI infrastructure.

A must-listen for engineering leaders, AI builders, and anyone navigating the challenges of deploying agentic AI at scale.

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