The fastest way to get a wrong answer from an AI database agent is to ask a simple business question. What was revenue last month? That sounds easy. The database has invoices, subscriptions, payments, refunds, credits, discounts, taxes, trials, failed charges, and test accounts. The model sees tables. Your business sees definitions. If those definitions are not part of the system, the model has to guess. Valid SQL can still be wrong A table called payments may include failed attempts. subscriptions may include trials. amount may be gross, net, pre-tax, post-tax, or stored in cents. created_at may mean invoice creation, payment capture, or customer signup. An AI agent can write syntactically valid SQL against all of that and still answer the wrong question. This is why natural-language SQL needs metric context, not just schema context. Approved views beat clever prompts A prompt can tell the model how to calculate MRR. An approved view makes the definition executable.…