Review the work your AI agents do. CLI + MCP.
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Agent Action Record #

Review date: Reviewed by: Agent / system: Task or period under review: Accountable human (name or role):


1. Intent — what was supposed to happen #

Instruction given (actual wording):

Success defined in advance as:

Authority granted to the agent:

Out of scope:


2. Action — what actually happened #

What the agent did (step by step, from logs where available):

What it produced or changed:

Where actual behavior differed from the instruction:

Stayed within granted authority? ☐ Yes ☐ No ☐ Unknown


3. Judgment — human in the loop #

Decisions the agent made on its own:

Of those, which should a human have made or seen:

Where a human actually intervened (approve / edit / override / redirect):

Points where it should have escalated and didn't:

Accountable for these actions:


4. Deviation — the gaps #

Gaps between intent and action, and why (root cause, not first answer):

Good deviations (correct departures from a flawed instruction):

Confidently wrong (high confidence + wrong — flag specifically):


5. Consequence — what it cost or risked #

Actual outcome (did the work hold up):

Harm / cost / risk — realized or narrowly avoided:

Downstream affected parties:

Expected failure if this run happened 100 times:


6. Change — what happens next #

Specific changes before next run (instruction / scope / authority / escalation / checkpoints):

Keep doing:

Should not be delegated to an agent at all:

Signal that the change worked: