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.