Your AI database agent needs dry-run mode

The dangerous moment in an AI database workflow is not always execution.

Often, it is the moment before execution, when nobody knows the blast radius yet.

The agent says a change is simple.

The SQL looks plausible.

The request sounds routine.

Then the query touches more rows than expected.

That is why production AI database agents need dry-run mode.

Dry-run is not a prompt instruction

“Check before you act” is not enough.

A real dry-run is enforced by the database or server-side tool layer. It lets the agent prepare a proposed operation, but prevents the side effect from happening until the system has produced a structured preview.

A useful dry-run result should include:

• operation type


• affected row count


• affected entity IDs or sample IDs


• before/after values for writes


• tenant or workspace scope


• policy checks passed or failed


• query budget impact


• approval requirement


• rollback or compensation hint


• audit event ID

The final execution should be deterministic. Not “let the model generate fresh SQL again.”

Longer version: Dry-run mode for AI database agents

The practical rule:

If the agent cannot preview the blast radius, it should not execute the operation.