Grounding AI Agents in Your Data Model

Every governance tool we already trust (ERDs, semantic layers, data catalogs, etc.) assumes someone already got the data model right before writing it down. That assumption is quietly breaking as AI starts drafting that first pass. This talk shows a citation-grounded pipeline, live: an agent drafts an ontology from a real dbt project, and a validator mechanically checks every claim it can, flagging rather than silently trusting the ones it can't. What will you learn: - A concrete, working pattern for citation-grounded AI-assisted documentation, adaptable to your own project - A precise mental model for what can be mechanically verified versus what always requires human judgment - A live demonstration of the pattern actually catching mistakes - An honest map of what this approach solves today, and what it deliberately doesn't