Every AI database answer needs a source trail

AI-generated database answers must include a source trail, as responses like "MRR is up 8%" are unreliable without knowing their origin. It emphasizes that errors often stem not from hallucinations but from using the wrong source, stale data, or incorrect scope. The key recommendation is to have the database layer produce provenance automatically, rather than relying on the AI model to invent it afterward.

An AI answer without provenance is just a confident paragraph. That may be fine for brainstorming. It is not enough for database answers that drive product, finance, support, or operations decisions. When an agent returns “MRR is up 8%,” the useful question is not only whether the number came from a database. The team also needs to know: Wrong database answers are not always hallucinations. Often they are grounded in the wrong source, an old replica, a stale metric definition, or the wrong tenant scope. Longer version: Query provenance for AI database agents The practical rule: Do not ask the model to invent provenance after the fact. The database/MCP layer should produce it as part of the tool result.