With great context comes great autonomy: leveling up your agent context
Why AI agents need governed context, not just more data, to be trusted.
Overview
Covers how dbt's structured context—governed models, tested metrics, the Semantic Layer—lets AI agents query warehouses accurately, and the difference between static definitions and dynamic reasoning. Shows combining dbt's context with Fivetran's context layer to power reliable agent results.
What you'll take away
- 01
Agents Schema writes what an agent learns about your business into open warehouse tables, so any AI client, Claude or Cortex, queries the same governed context over MCP.
- 02
Fivetran Context Layer links knowledge, operational, and analytics sources into one ontology, so a revenue table and a revenue doc get connected instead of living as separate facts.
- 03
A built-in traces feature flags where an agent's confidence was low or context was missing, giving teams a concrete way to improve context over time instead of guessing.
Session speakers
Ben Moser
dbt LabsKevin Kim
Fivetran