With great context comes great autonomy: leveling up your agent context
An agent is about to query your warehouse to answer a question, with total confidence and zero doubt. Thanks to dbt structured context — governed models, tested metrics, the dbt Semantic Layer — it's not guessing anymore. It knows what "active" means, which revenue table to trust, what the business logic says. dbt structured context hands your agent a map, and every road on it leads somewhere real.
But a map only shows the roads someone has already built. Leveling up means giving agents the freedom to reason beyond fixed definitions, pulling in whatever structured or unstructured context actually answers the question at hand. In this session, we'll show a live demo of the dbt structured context and Fivetran context layer turning raw metadata, governed semantic models, and real usage into the agent schema that powers accurate results, using the tools you already run: Fivetran and dbt.
Check out more sessions
- Lightning talk
Inside The Agentic Data Stack at Bilt
Ben Kramer / BiltSam Masling / BiltView session - Breakout session
A dbt "logic mesh" with packages: standard model and metrics across 25 autonomous organizations
Koen Verburg / Greenpeace InternationalView session - Breakout session
Governed by default: how data teams at Nordstrom turn dbt governance into an AI advantage
Priya Tanwar / NordstromNadine Bruxel / NordstromView session
