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.
source & further reading
dev.to — original article
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