Why dbt Labs thinks AI agents will finally make the semantic layer stick
Why AI agents can make semantic layers stick, if teams can solve the trust problem.
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Why AI agents can make semantic layers stick, if teams can solve the trust problem.
KOHO explains why AI self-serve analytics needs a strong data culture and a governed dbt foundation first.
Okta rebuilt how it governs context for AI agents, using dbt as the single source of truth.
dbt Wizard's designer and engineering lead show how the agent validates its own work before code ever reaches production.
dbt v2 bundles adapters, speeds up parsing, and replaces manifest.json with smaller, queryable files for people and AI.
We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.
Ramp shares the agents, sandboxes, and dashboards it built by treating internal AI as analytics engineering.
Verisk modernized decades of insurance data, from mainframe to dbt, to get AI-ready.
Careem's AI agent writes and validates dbt models end to end before a human ever reviews the PR.
How a data scientist built an AI context layer for organizational knowledge a dbt project alone can't hold.
Benn Stancil uses the Hugging Face hack to explain why the most useful ideas come from the weirdest connections.