TL;DR
A separate reviewer should check AI-generated code against the original requirement, not simply agree with the coding agent’s explanation. Give that reviewer the changed code, relevant context and test evidence. Ask for a concrete reason behind each finding, then verify proposed fixes before applying them. Reviewers can miss defects, but they can also reject correct code. The aim is a decision someone can explain and challenge, with unresolved questions left visible for the person responsible for the change.
In this article
Last updated October 15, 2026
6 min read
An independent review can be wrong in either direction. It may approve a defect, or it may persuade the author to replace working code with a broken “fix.” Separating the reviewer from the worker creates an opportunity to challenge the change. Whether that challenge helps depends on what the reviewer checks and how the finding is verified.
For AI-assisted work, a new chat or a different model is only part of that arrangement. The reviewer still needs a reliable description of the intended behaviour and access to evidence beyond the worker’s summary.
Test the reviewer’s proposed fix
Consider this declared requirement: allow integer values at or above a configured limit. A Python demonstration executed two implementations against six examples. The original uses value >= limit. A proposed change uses value > limit.
Both implementations reject values below the limit and accept values above it. They disagree exactly at the boundary.
Observed results with a limit of 10
| Value | Expected | Before | After |
|---|---|---|---|
| 9 | Reject | Reject | Reject |
| 10 | Allow | Allow | Reject |
| 11 | Allow | Allow | Allow |
The same three positions were tested around a limit of 20. The original met all six expected outcomes. The proposed fix failed both equality cases. This was a constructed code example, not an observed AI review or customer incident. It establishes the behaviour of these two expressions for the recorded integers, not the reliability of a model.
The useful review finding would identify the exact condition, connect it to the requirement and show the differing result. “Boundary handling looks wrong” does not provide enough information to decide which implementation should change. If the requirement were instead strictly above the limit, the expected equality result would change too. The intended behaviour must settle that question.
A reviewer can reject correct code
A 2026 study by Jin and Chen, Are LLMs reliable code reviewers?, examined correct and buggy implementations drawn from HumanEval, MBPP and QuixBugs. In that setup, requiring more explanation or proposed repairs could reduce acceptance of buggy code while increasing rejection of correct code. The study also evaluated a filter that checks proposed fixes through execution.
Those are benchmark findings for the tested models and prompts, not a measured error rate for your repository. They support a practical caution: a detailed critique is still a claim to investigate. Do not count every raised finding as a defect successfully caught.
A useful review record distinguishes confirmed defects, open questions and optional improvements. For a functional defect, preserve the input, expected outcome, observed outcome and relevant revision. For a design concern that cannot be reduced to one test, explain the dependency or maintenance cost and the evidence behind it. A reviewer should be able to say what would change their conclusion.
Give the reviewer an independent starting point
The worker’s explanation can help someone navigate a change. It should not become the only description of what success means. Supply the accepted request, relevant constraints and examples separately, including any decision that changed the scope during implementation.
There is a related risk in tests. The July 2026 preprint On the risk of coding before testing reports cases in which errors in generated implementations carried into subsequently generated test artifacts. Its abstract describes a bounded study of coupled code-and-test workflows. It does not prove that every test written after implementation is biased, or that another model will remove the problem.
For a review, inspect where expected results came from. In the boundary example, the expectation at 10 follows from the words “at or above.” Deriving the expected answer by running the implementation under review would merely reproduce that implementation’s choice. The guide to testing from the specification develops this distinction.
Independence also requires enough repository context. A reviewer shown only the changed line may miss a caller that already validates the input, or one that relies on the previous behaviour. Include the surrounding function and relevant callers when they affect the finding. A fresh context with essential facts removed is poorly informed, even if it contains none of the worker’s reasoning.
Make findings possible to resolve
This format gives the author something specific to reproduce or challenge. It also lets another reviewer check whether the conclusion follows from the evidence. The wording need not be elaborate; the important part is the connection between behaviour and requirement.
Avoid letting an AI worker apply every proposed repair automatically. First establish that the finding is valid. Then examine the repair as a new change, because resolving one concern can introduce another. Keep the original finding and its resolution together so later reviewers can see why a suggestion was accepted, revised or rejected.
Google’s code-review standard prioritises technical evidence over preferences and advises reviewers to distinguish optional polish from blocking concerns. It also recommends recording the result when a disagreement is resolved through discussion. That provides a useful basis for handling AI findings without turning every stylistic suggestion into mandatory work.
Keep final review tied to the actual revision
Record which code was reviewed and which checks were run. If a proposed fix changes that code, decide what must be reviewed again. An approval of the earlier version does not describe the new one, even when the edit came from the reviewer.
Set the review scope explicitly. A check of one boundary condition says little about unrelated concurrency, data access or deployment behaviour. Match additional review to the affected area, and leave uncovered concerns visible. The article on risk-based AI development lanes explains how to vary the evidence required by the change’s consequences.
If your team needs help making AI-assisted changes easier to verify and own, request an AI Delivery Programme conversation.
Frequently Asked Questions
Why should AI-generated code receive an independent final review?
It gives someone who did not produce the change a chance to question its assumptions and consider its effects on the existing system. Use that review where the consequence of an error justifies it. Independence describes a responsibility and a source of scrutiny; it is not a certificate attached to the reviewer.
What makes an AI code review independent from the coding agent?
The reviewer can reach and preserve a different conclusion, including a refusal to approve. The worker must not be able to turn that unresolved verdict into acceptance simply by rewriting its own summary. The workflow needs an explicit path for deciding what happens when they disagree.
What if a reviewer raises an edge case that seems impossible?
Identify the assumption that excludes it and where that assumption is enforced. If the case can occur, assess its consequence and the cost of addressing it. If it cannot occur under the agreed contract, record why. Neither “unlikely” nor “the reviewer mentioned it” settles whether work is required.
Can the reviewer fix the code and approve it afterward?
They can propose a repair, but that makes them an author of the repair. Where the workflow requires a separate final reviewer, route the revised code to someone else. Do not preserve the appearance of separation by leaving role labels unchanged after responsibilities move.
What if only one engineer understands the affected module?
Use that engineer to explain the constraints and help another reviewer follow the change. Keep the change small enough to inspect and record remaining knowledge gaps. A second person who cannot understand the relevant behaviour adds little assurance merely by clicking approve.
How can you tell whether an AI reviewer is worth keeping?
Track useful confirmed findings, invalid findings, missed defects discovered later and time spent investigating comments. Compare similar kinds of changes and record the reviewer configuration. A large comment count may reflect noise rather than useful scrutiny, while a low count may reflect either clean changes or missed problems.
