AI Database Agents Need Review Queues to Handle Uncertainty, Not Just Approval Buttons

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AI database agents operating in real workflows often encounter ambiguous metric definitions, missing filters, or partial results where a simple approve-or-reject button is insufficient. Review queues are designed to address situations where the system lacks enough certainty to act, rather than just confirming whether a prepared action can proceed. A well-structured review item should capture the original query, user scope, proposed interpretation, tool call attempt, and suggested next actions such as approving, narrowing, or rerouting. Beyond resolving individual cases, review queues can feed back into product improvement by highlighting recurring ambiguities that signal gaps in schema context or metric definitions. The goal is to make uncertainty inspectable and actionable, rather than turning human oversight into a bottleneck.

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