Alyssa Lee

Most enterprise AI agent failures don't look like model failures. The model is fine. It just doesn't know which of four revenue tables finance actually uses, who owns it, whether last night's pipeline ran, or that one column was deprecated in March. That knowledge exists inside the organization - scattered across technical schemas, pipeline telemetry, business definitions, and org charts - and almost none of it reaches the agent.

This talk is about assembling those four kinds of metadata into a single, model-agnostic context layer that agents can query, and what it actually takes to do that inside a large enterprise. I'll cover the architecture briefly, then spend most of the time on the parts that are harder than the architecture: deciding what context is trustworthy enough to serve, keeping it fresh, exposing it to agents over MCP, and the organizational work of getting teams to maintain context they never see themselves using.