Every AI coding tool can generate code. Very few can generate the right code for your organization, because they’re missing context. They don’t know why your team chose Redis over DynamoDB, what the team decided in a Slack thread from two months ago about the auth migration, or which architectural patterns your principal engineers actually enforce in review.

This talk is a practitioner’s guide to building a context engine: the reasoning layer that continuously synthesizes organizational knowledge across disparate sources into unified, queryable understanding. I’ll walk through the problems you actually have to solve — reasoning across systems that don’t agree with each other, searching globally before you can reason, maintaining identity-scoped permissions so every user and agent only sees what they should, and personalizing results based on who’s asking and what they’re working on. These are the engineering challenges that make naive RAG fall short, drawn from real lessons building this at scale.