Extract, enrich, evaluate: a playbook for building an AI-Ready context layer

The context your agents need already exists across your catalog but is not agent-ready. Raw schemas, lineage, BI definitions, and query history are dense enough to burn token budget on every question and produce a different answer every session. The cold-start problem is compression: turning what's already there into portable context an agent can lean on cheaply and consistently. We'll walk through how DataHub helps you bootstrap reliable context at scale, and share a practical framework for your team to build ongoing evaluation of that context, so agents keep getting it right as your data, schemas, and business definitions evolve.