dbt State and agent-ready data: why declared meaning comes first

Agents dominated dbt Summit, but does smart orchestration or a context layer make data agent-ready on its own? The episode argues no. Fanatics Betting and Gaming saw about 0.2% model reuse from dbt State until it declared source freshness and model-level SLAs, which raised reuse to roughly 15%. The same pattern runs through Fivetran's Context Layer, AI audit trails and token costs. Listeners learn how dbt State is billed per daily active target table, including tests, why the Context Layer's beta status needs checking in writing, how git history serves as an audit trail, and how dbt models served through the MCP server cut Gong transcript tokens by 20x or more. 0:00 Fanatics and the 0.2% dbt State reuse rate 2:21 dbt State pricing and per-test charges 3:53 Fivetran Context Layer: early access risks 6:26 Auditing AI agents without a rulebook 7:49 Why token costs belong to data teams 9:17 Declare meaning before agents act