Code Review in a Minute
Reviewing AI-Generated Code
How to review agent-produced changes when volume is high and PR descriptions may overclaim.
In one sentence
Reviewing AI-generated code means separating the agent's claims from repository evidence, then applying the same risk checks you would apply to any large change.
Why it matters
Agents can edit many files quickly. Commit messages and summaries often describe intent, not what landed. Reviewers who trust the narrative without the diff approve risk they never inspected.
How it works
- Treat the agent summary as a hypothesis, not as evidence.
- Map claimed outcomes to concrete diffs, tests, and config changes.
- Prioritize high-risk surfaces: auth, data deletion, migrations, dependency upgrades, generated tests that only mirror happy paths.
- Check for bulk mechanical edits that look correct but skip edge cases.
- Require evidence for behavior claims: failing then passing tests, typed contracts, or reproducible commands.
Example
An agent says it “refactored auth safely.” Useful review questions:
Which files in the auth path changed? Did session invalidation tests change? Did dependency or middleware order change? If those answers are not in the PR evidence, the claim is unsupported.
What this proves
Careful review can establish that specific files changed, that specific tests exist or changed, and that certain claimed behaviors are or are not reflected in the diff.
What this does not prove
Passing a human skim does not prove the agent understood the domain, that production behavior is unchanged where claimed, or that the change was the cheapest correct solution.
Last reviewed 2026-09-06. Title for citation: Code Review in a Minute: Reviewing AI-Generated Code.