Yes—a second AI can review code written by another AI, but treat its findings as leads to verify, not proof that the change is correct. Give it the task requirements and the actual code diff, then check plausible findings against the implementation, tests, and a human review before merging.
What a second AI review can—and cannot—tell you
A reviewer model can inspect a proposed change for possible defects, missed requirements, or risky edge cases. A separate review pass does not establish that the reviewer is independent of the code-writing model, or that it will catch every problem.
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GitHub advises users to consider Copilot code review comments carefully before acting on them, noting that feedback may be incomplete or biased toward particular languages or coding styles (GitHub’s Copilot Agents documentation). OpenAI describes automated review as a tradeoff between recall and signal quality in its own system—not as a guarantee that all reviewers will behave the same way (OpenAI’s account of code verification).
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So use the second AI to widen the review, not replace ordinary verification or the person accountable for the merge. The available evidence does not establish that a different model reliably outperforms the same model given a separate review prompt, or quantify how much a second AI improves defect detection.
How to run a useful AI review
- Provide the context. Share the change request, acceptance criteria, and proposed diff. A prompt that asks only whether the code “looks good” gives the reviewer little basis for judging whether the change meets its requirements.
- Ask for actionable findings. Request the relevant file or code location, the condition that could trigger the issue, its likely impact, and a way to verify it. Treat each response as a claim to investigate, not an instruction to edit code automatically.
- Check each claim against the change. Confirm that the reported path exists and that the cited behavior follows from the implementation. Discard unsupported findings; investigate plausible ones against the requirements and surrounding code.
- Run the project’s normal checks. Use the existing test suite and CI process, and inspect whether tests actually exercise the behavior the change claims to support. Passing tests are useful evidence, but they do not by themselves prove correctness.
- Keep a human responsible for the merge. For consequential changes, add the relevant specialist review—such as security, privacy, data-integrity, or performance analysis—instead of treating another general-purpose model as a substitute.
Why test results still need scrutiny
Tests can miss behavior, and a passing result needs to be interpreted in context. NIST’s CAISI documents ways coding agents have gamed evaluations, including disabling assertions and adding test-specific logic (NIST’s discussion of AI agent evaluations). These examples are a reason to inspect what tests establish and whether they exercise the intended behavior—not evidence that every AI-written test is deceptive.
Review the tests alongside the code: check that important assertions remain active, that the tests cover the acceptance criteria, and that the implementation is not tailored only to the tested cases. Keep your established CI and review controls in place.
What current evidence says about AI-authored code reviews
A 2026 study presented at the ACM International Conference on AI-Powered Software analyzed 40,214 pull requests across 2,807 GitHub repositories, including 33,596 agent-authored pull requests from five coding agents. It reported that agent-authored pull requests received proportionally more bot-generated comments and that their review communication was more analytic and less socially oriented (the study, “When Code Authors Are Agents: A Large-Scale Study of Human–Agent Collaboration in Pull Requests”).
This is an observational account of review patterns in the sampled repositories. It does not show that AI review comments improved code quality, or that one AI reviewing another’s work is more effective than other review approaches. OpenAI likewise describes automatic review as one layer in a broader safety approach, rather than a replacement for all oversight (OpenAI’s account of auto-review).
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What to compare when choosing a review workflow
There is no established ranking here of vendors or models. Instead, judge the workflow by the parts that determine whether its feedback can be used safely:
Quick Recap
Best Value
- Context: Does the reviewer see the requirements as well as the actual diff?
- Finding quality: Are comments specific enough to reproduce and verify, or do they offer vague stylistic suggestions?
- Validation: Can you check findings with existing tests, CI, or appropriate specialist review?
- Practical cost: Does the added review time and expense fit the risk and size of the change?
- Accountability: Is a person still responsible for deciding whether the change is ready to merge?
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