The Tool Desk
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What an AI-versus-AI code review can—and cannot—do
Making an AI “argue with itself” is a review technique: one pass produces or changes code, while another looks for reasons the change might fail. The critic can surface a different perspective and make potential weaknesses easier to inspect. It can also miss a defect, invent one, or offer a confident but mistaken rebuttal.
OpenAI has discussed debate as a safety proposal in which agents present competing arguments and a human judges which is stronger. That makes arguments inspectable; it does not establish that the apparent winner is true or that debate reliably catches code defects. OpenAI’s work on AI-written critiques also addresses limits in critiques and the difficulty people can have assessing challenging tasks. OpenAI’s discussion of AI-written critiques and its debate proposal provide useful context, but neither is a code-review guarantee.
In practical terms, the useful output is a set of specific claims to check: “this branch can receive an empty value,” for example, or “this update may overwrite a concurrent change.” A second generated opinion is not equivalent to a test, a static-analysis result or a reviewer who understands the system.
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A practical workflow while the coding assistant works
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Bound the change
Give the coding assistant a focused task and relevant constraints. Keep the change small enough to inspect; broad, mixed changes make it harder to connect a suspected flaw to a particular decision.
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Run an independent critique pass
After the code is drafted, ask a critic to inspect the relevant diff and context. When possible, use a separate model or agent that did not produce the change. A separate reviewer may reduce shared assumptions, but it still may lack repository or architectural context. A same-model self-critique is convenient, though it may repeat the author’s blind spots.
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Demand actionable findings
Ask for likely bugs, unhandled edge cases, incorrect assumptions and—where relevant—security or data-integrity risks. Require each finding to point to a file and code location, describe a plausible failure path, and distinguish blockers from suggestions. Ask the critic to say when it lacks enough context rather than fill gaps with guesses.
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Ask the author to respond, then verify
Have the coding assistant address each finding with evidence from the implementation or tests. Treat both the critique and the rebuttal as claims: inspect the code, run relevant tests, and use static analysis or other suitable tools. Escalate high-impact or system-specific questions to a human reviewer with the needed context.
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Make the final decision yourself
Decide whether the change meets the product and system requirements, not just whether one agent persuaded another. Review can happen in a pull request, but it can also be part of ongoing refinement. Tests, tool feedback and team review answer different questions; none alone proves overall correctness.
This is a practical synthesis of tool-interactive critique and focused review guidance, not a protocol shown to improve outcomes in every project. Microsoft Research’s CRITIC work studies critique combined with tools and feedback; the key distinction is that a tool can provide evidence beyond another generated opinion. Martin Fowler’s discussion of review, testing and smaller changes and guidance on focused AI review prompts offer complementary workflow advice.
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Choose the review method for the question
These approaches are complementary rather than interchangeable. Sources describe the approaches, but do not establish a head-to-head trial proving which produces the best code review.
| Approach | What it can contribute | What to watch |
|---|---|---|
| Same-model self-critique | A quick second pass that can list assumptions and potential edge cases. | The author and critic may share blind spots; generated criticism is not independent verification. |
| Separate model or agent | A reviewer less directly tied to the original drafting pass. | Its independence does not ensure access to the project’s architecture, constraints or full repository context. |
| Tests and other tools | Executable or analyzable feedback about behavior and specified properties. | Checks cover only what they are designed to examine; passing results do not prove the change is correct in every relevant situation. |
| Pull-request review | A structured place for people to inspect a change and discuss its rationale. | Its usefulness depends on the scope of the change, available context and quality of review; it is one review format, not the whole process. |
| Ongoing team refinement | Review and feedback can be incorporated throughout development, not only at a final approval step. | It still requires suitable checks and decisions by people who understand the system. |
For pull-request tradeoffs and review practice, see Fowler’s Pull Request discussion. GitHub’s adversarial-review repository is an implementation example of multi-agent review, not independent evidence that the approach improves code quality.
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Prompt for findings you can actually check
Adapt this prompt to the language and risk of the change. Include the diff, relevant surrounding code and constraints; a critic cannot reliably assess context it has not been given.
Review this change as a skeptical code reviewer. Do not rewrite it yet.
Context and constraints:
[Relevant requirements, invariants, interfaces, and compatibility constraints]
Inspect the diff and relevant surrounding code for:
- likely bugs and unhandled edge cases
- assumptions that may be false
- security or data-integrity risks, if applicable
For each finding, provide:
- severity: blocker or suggestion
- file and line or code location
- the assumption or behavior at issue
- a plausible sequence that would cause failure
- what evidence or test could confirm or rule it out
Separate confirmed observations from hypotheses. If context is missing, state what is missing. Do not treat the absence of a finding as proof that the change is correct.Fowler’s Sensible Defaults guidance similarly emphasizes explicit context, focused review requests and structured findings. The prompt’s value is not that its wording guarantees a good review; it helps turn an open-ended “check this code” request into claims that can be examined.
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