An AI-generated code review checklist should answer one question: what evidence makes this change safe enough to accept? Compiling code, a convincing Agent summary, and another model saying “looks good” are not interchangeable with that evidence.
GitHub's Copilot overview keeps review and approval with the user. The workflow below is our suggested acceptance method, not a claim that an automated reviewer can certify correctness. Documentation checked September 29, 2026.
Start from behavior, not the summary
Restate the requested outcome and identify the exact changed code responsible for it. Read the diff before accepting the Agent's explanation of the diff.
Consider a hypothetical endpoint that deletes a saved project. “The delete button works” is not a complete requirement. The owner should be able to delete the intended project; a different user must not be able to delete it; an interrupted request must not silently corrupt unrelated records.
Write those expected outcomes before reviewing tests. Otherwise, a test can validate the implementation's mistake instead of the intended behavior.
Use risk to choose the depth of review
This is a starting checklist, not a substitute for a security specialist on high-risk systems. Escalate uncertainty rather than turning a missing check into an assumed pass.
Check whether the tests could catch the bug
For the deletion example, include an authorized success, an unauthorized attempt, and a missing-project case. Verify expected behavior against the product's actual rules.
When practical, demonstrate that the relevant regression test fails on the old implementation and passes after the fix. Check that a mocked authorization layer has not removed the very permission boundary you intended to test.
Tests written by the same Agent are useful proposals, not independent truth. For repeatable model or workflow comparisons, build a separate coding Agent regression set.
Hand over an acceptance record
Ask the Agent for a short record containing:
- the behavior changed and the files responsible;
- checks actually run, their results, and any checks not run;
- remaining risks and assumptions;
- the exact revision reviewed;
- any required human approval before merging or deploying.
After multiple contributions are combined, rerun relevant checks on the combined result. A review of one branch does not validate a different final diff. The team handoff guide helps keep that evidence attached to the work.
Keep generation and approval separate
A second Agent can look for counterexamples, but someone must still decide whether evidence covers the risk. Narrower changes make that decision easier; a large unsolicited refactor makes it harder.
If you want to organize implementation and review in one hosted project, see how Agent.Space works. Open Agent.Space to start a bounded task, then apply the same review standard regardless of which Agent produced the patch.