Lawrence Coburn 
Taylor McLoughlin 
Brandon Catcho AI agents keep getting smarter. Their output still depends on the data they can reach.
We learned this the hard way building an AI support agent for our own product business. The context it needs lives in five places: Intercom, years of email, billing, our product database, and third-party recorder logs. Each system holds a piece of the answer. None holds the whole thing.
In this one-hour session, we’ll share what we’re seeing from our own build, from clients, and from a deep dive with a team running three support agents across 400 to 500 tickets a day for a consumer app with over a million users.
We’ll cover:
- How to inventory where each agent’s context actually lives
- Distilling history into skills vs. stitching live systems together
- Why vague error messages break agents, and how to fix them
- Starting scrappy (Google Drive) vs. building for scale (Snowflake)
- Capturing context that lives nowhere
For operators, Chiefs of Staff, and technical leaders standing up agents inside their companies.