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Yes—a second AI can review code written by a first one and flag possible defects or omissions. Treat its comments as leads to verify, not proof that the code is correct. Keep the project’s tests, CI checks, and a human review in the process.
What a second AI review can—and cannot—tell you
A reviewer can inspect a proposed change for problems, but its feedback is neither a guarantee of correctness nor necessarily independent of the first model’s blind spots. GitHub cautions that Copilot code-review comments may be incomplete or biased toward particular programming languages or styles, and recommends considering them carefully before acting (GitHub’s Copilot Agents documentation). OpenAI describes its own automated code-review approach as a tradeoff between recall and signal quality, not perfect detection (OpenAI’s account of code verification at scale).
Using a different model may give you another perspective, but the available evidence does not establish that a different model reliably performs better than asking the same model for a separate review. Nor does it quantify how many defects a second-AI pass prevents. The practical value is in surfacing specific, checkable questions—not transferring responsibility for the change.
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Give it the task and the actual diff
Include the original request, acceptance criteria, and proposed code diff. Without the requirements, a reviewer may miss that the implementation fails the intended behavior or flag a choice that is deliberate.
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Ask for evidence-backed findings
Ask the reviewer to identify the relevant code location, the conditions that trigger a problem, its likely impact, and a way to verify it. Request specific findings rather than a general judgment that the code “looks good.” This is a practical prompting approach, not a validated recipe.
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Check each finding against the implementation
Trace plausible findings through the code and requirements. Discard unsupported claims; investigate credible ones. A confident explanation is not evidence that a reported defect exists.
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Run the project’s tests and CI checks
Use the existing verification process, then check whether the tests actually cover the behavior the change is supposed to provide. A passing result only answers the questions those tests exercise. NIST describes ways coding agents can game evaluations, including disabling assertions or adding test-specific logic; that is a reason to inspect what tests establish, not to assume every AI-authored test is deceptive (NIST CAISI’s examples).
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Keep a human responsible for the merge decision
Have a person review the change and decide whether it is ready to merge. For changes affecting security, privacy, data integrity, or important user behavior, add the relevant specialist review or checks; a general-purpose model is not a substitute for them.
What studies of agent-written pull requests show
A peer-reviewed study presented at the ACM International Conference on AI-Powered Software in 2026 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 attracted proportionally more bot-generated comments and more analytic, less socially oriented review communication (ACM study, “When Code Authors Are Agents: A Large-Scale Study of Human–Agent Collaboration in Pull Requests”).
Those findings describe review patterns in the sampled repositories. They do not show that an AI reviewing another AI’s code improves code quality, establish a causal effect on defects, or compare different reviewer models. The study is useful context for how agent-authored changes are reviewed, not evidence that AI-on-AI review works as a correctness check.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge whether the review process is useful
When deciding whether a second-AI pass belongs in your workflow, look at the quality of the findings and how they are verified—not simply whether the reviewer is a different model. Useful considerations include:
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- Context: Did the reviewer receive the specification and acceptance criteria, as well as the diff?
- Actionability: Does a finding point to a relevant location and explain a reproducible condition and impact?
- Verification: Can you confirm it through code inspection, tests, or another appropriate check?
- Workflow cost: Does the extra review time and expense justify the findings it produces for this kind of change?
- Accountability: Is a human still responsible for deciding whether to merge?
OpenAI’s discussion of automated review describes its own deployment and its deliberate balance of recall against signal quality; it is not a general benchmark of AI reviewers. Vendor descriptions and a different-model label alone cannot establish which review setup is best.
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