Code Review for AI Code: Six Patterns the Classic Review Misses

A review of AI-generated code has to look for different things than a classic review. An AI rarely produces typos or careless mistakes. It produces code that looks plausible and systematically leaves certain things out in specific places: permission checks, error handling, and a check against logic that already exists.

Why does the classic code review work worse with AI code?

Because it is built for a distribution of errors that no longer applies in this form. Human code typically contains lapses of attention: swapped variables, forgotten edge cases, unclear names. These errors look wrong, and that is exactly what a reviewer is trained to spot.

AI code looks correct. Names are consistent, the structure is conventional, comments are present. The eye finds nothing to stop on. In the Stack Overflow Developer Survey 2025, 66 percent of respondents named "almost right, but not quite" as their biggest frustration when working with AI tools, which is exactly the description of a class of error that does not stand out just by looking.

On top of that comes volume. A review of 800 generated lines does not get the attention of 80 hand-written ones. Past a certain size, every review turns into a sample without anyone deciding that.

What do I need to watch for especially in a review of AI code?

Six patterns that show up disproportionately often in generated code.

• Missing permission check. The business logic is correct, but the question "is this user allowed to do that?" was never asked. Broken access control remains unchanged at first place in the OWASP Top 10:2025.
• Swallowed errors. An empty catch block, an ignored return value, a default value that disguises a failure as a success. Its own OWASP category since 2025 (A10).
• Newly invented logic next to existing logic. The model does not know your helper function and writes a second one. Both work, both now need to be maintained going forward.
• Unnoticed new dependencies. Check the package files in the diff just as carefully as the source code.
• Tests that confirm the code instead of checking it. Assertions derived from the current behavior cement existing bugs in place, see test coverage for AI code.
• Changes that are too broad. If files unrelated to the task get touched along the way, that is not cleanup, it is an unreviewed side effect.

How should a review of AI code proceed?

In four steps, where the first two run by machine and the human only starts at the third.

• Step 1, the machine clears away the obvious. Formatting, static analysis, security rules, dependency and license checks, a test run. Anything a tool can decide should never cost a human's time.
• Step 2, check the scope. Does the diff match the task? Changes that are too broad get sent back before anyone reads them. This rule saves more review time than any other.
• Step 3, a human checks the intent. Does the change solve the stated problem? Does it fit the architecture? Does this already exist elsewhere? Have permissions and error cases been considered?
• Step 4, proof instead of a promise. Approval requires the output of the test run, not a statement that it ran.

Can an AI take over code review?

As a first pass yes, as the last pass no, and for a structural reason.

AI-assisted reviewers are good at what can be recognized from the code itself: patterns, inconsistencies, obvious security issues, missing error handling. They are unreliable on the one question that really matters: is this the right behavior? That question cannot be answered from the code alone, only from knowing what the software is for.

On top of that comes a circular problem that is easy to miss: if the same model both writes and then reviews, it shares the same blind spots both times. A second model helps to some extent, but it does not replace the person who knows the business problem.

How much time should a review cost?

Less than it does today, if step 1 and step 2 really run by machine. In the Stack Overflow Developer Survey 2025, 45 percent of respondents said they lose significant time debugging AI code. Part of that is review time spent in the wrong place: people check formatting and style while the permission gap slips through.

The solid rule of thumb is: what a tool can decide, no human should decide. What only a human can decide, no tool should decide. Everything else is waste on one side and risk on the other.

Frequently asked questions about code review

Do I have to disclose that code was created with AI?

There is currently no general legal obligation to do so. For the review itself, the information is useful anyway: it guides what to look for. Many teams handle this with a label on the pull request rather than a rule.

How do I handle very large generated changes?

Send them back and have them split up. A diff too large to read does not get checked more carefully because it is urgent, it just gets checked more superficially, without anyone saying so out loud.

Is a review by one person enough?

For most changes, yes. For authentication, payment, schema changes, and deployment, a second pair of eyes is the cheaper insurance.

What if nobody on the team understands the generated code?

Then that is the finding, not an obstacle to the finding. Code that nobody understands is not maintainable regardless of whether it is correct, and it should not be merged.

A human checking formatting is an expensively hired linter. Code Guardian moves the machine checks earlier: an audit right after the change, along fixed layers such as logic, redundancy, and security, so that the review is left with the questions that actually need a human.

Sources

• Stack Overflow Developer Survey 2025, "AI" section (66 percent "almost right", 45 percent time lost debugging).
• OWASP Top 10:2025, categories A01 and A10.

As of July 27, 2026. General professional context, not legal advice.