Connecting an AI agent to a database is the easy part. Getting useful answers is harder. The model needs context before it can turn a natural-language question into a safe and accurate query. Not unlimited context. The right context. Without it, the agent guesses: which tables matter how joins work what metrics mean which columns are sensitive whether the result is fresh enough to trust That is how a simple business question becomes a wrong answer with high confidence. A schema dump is not schema context A raw list of tables and columns helps a little. It is not enough. Production schemas contain implementation history, deprecated fields, naming inconsistencies, duplicate concepts, and tables that should never be queried directly by an AI workflow.…