Your AI Agent Doesn’t Need a Smarter Model — It Needs to Know Your Domain

Every talk this year is about making AI agents do more — write more code, take more actions, run more autonomously. Yet many enterprise AI pilots still fail to deliver production value, and the usual suspects — model choice, prompts, infrastructure — are often not the real culprit.

The agents aren’t failing because they aren’t smart enough. They’re failing because they don’t know your domain. RAG can’t retrieve what was never written down, and the knowledge your agent needs most — undocumented conventions, operational shortcuts, and tribal fixes — is often exactly what your systems can’t reach.

This talk reframes agent reliability as a context problem, not a model problem, and introduces Demand-Driven Context: instead of documenting everything up front and hoping it helps, you let the agent fail on real work and use each failure as a signal for what knowledge is missing.

Drawing on real enterprise data — including a case where only about 20% of demanded knowledge was fully documented — I’ll show why the future of reliable agents isn’t just a bigger model, but treating context as a first-class engineering discipline. You’ll leave with a practical lens for diagnosing what your agents don’t know, and a method to start fixing it.

KEY TAKEAWAYS
– Why agent reliability is a knowledge problem, not a model or prompt problem.
– A lens for measuring what your documentation actually covers (usually far less than you think).
– Demand-Driven Context: using agent failures as the signal for what to curate.
– A practical, model-agnostic method you can apply to your own systems.