AI database agents should not have only two modes:
- answer
- fail
Real workflows have uncertainty.
A metric definition is ambiguous. A tenant filter is missing. A result is partial. A query is safe to prepare but not safe to execute. The model has enough evidence to suggest an answer, but not enough authority to act.
That is where human review queues belong.
Approval gates answer: “Can this prepared action proceed?”
Review queues answer: “What should happen when the system is not sure yet?”
A useful review item should include:
- the original question
- user/workspace/tenant scope
- proposed interpretation
- tool call or query attempt
- schema/context version
- policy reason
- result evidence
- safe next actions: approve, narrow, reject, reroute, or turn into an approved view
The review queue should also feed the product loop. If the same ambiguity appears repeatedly, the fix is probably better schema context, metric definitions, approved views, or tool result contracts.
Longer version: Human review queues for AI database agents
Human review should not be a bottleneck. It should be where uncertainty becomes inspectable.
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