Brandon Waselnuk

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 earlier today 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 ingests & 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.

In this expo talk we'll give you a free context engine simulator, open source tools, and demo how a context engine works. See how modern engineering workflows with agentic loops and goals produce better quality code and reduce token burn. RAG, while useful, leaves context gaps for humans and agents. A context engine fills those gaps by including real-time, relational, personalized, and permission aware techniques to get high-signal context to humans and agents at runtime.