How should a team review AI-generated code before it ships?
Harness Institute reviews AI-generated code before it ships by checking the task brief, the diff shape, the test evidence, and who owns the merge. A polished diff that passes tests can still hide a context mistake. Reviewers reject unreviewable agent work early instead of rubber-stamping it.
How should a team review AI-generated code before it ships?
Harness Institute uses four stops before merge: the brief is attached, a test ran and the link is in the pull request, a person who did not prompt the agent read the diff, and architecture or data changes stay with a human owner. If any stop fails, the change does not ship.
Review the work, not the demo
AI-generated code can look polished while hiding context mistakes, weak tests, or risky abstractions. Reviewers need to inspect the task brief, diff shape, verification evidence, and ownership boundary before they judge whether the change is acceptable.
The review loop we teach
Teams practice small diffs, targeted tests, failure reproduction, second-pass critique, and Codex code review prompts that force the agent to explain risks instead of simply defending its own output.
What becomes repeatable
The team leaves with a review checklist, examples of acceptable evidence, escalation rules for architecture and security decisions, and a shared vocabulary for rejecting unreviewable agent work early.
Related training topics
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