How the Claude Code Team Reviews Code

How does the Claude Code team review code?

The team’s stated aim is to use less human review time without increasing the number of incidents or changes that must later be undone. It says much of the code reading is now done by machines, rather than relying on people to examine every line ¹.

That is a response to the volume of code changes associated with coding agents. The team says manual, line-by-line review becomes hard to sustain when changes arrive quickly ¹. Its answer, as described in the blog, is to combine machine reading with human oversight and to make recovery from a bad change less difficult.

The account is about the Claude Code team’s own workflow. It is not a complete operating manual, and it does not set out exactly which kinds of changes receive which kinds of review.

Why use machines for part of code review?

A code review is an examination of a proposed software change before or after it is accepted. It can help identify defects or other problems, but a person’s time is limited. When the number of proposed changes rises, reading every line by hand may become a bottleneck ¹.

The team’s approach shifts a large share of that reading to machines, according to its blog ¹. In principle, this leaves people with more attention for judgments that benefit from human context. However, the supplied account does not describe the particular automated checks, how they are tested, or how the team decides when a person must take a closer look.

Automation therefore should not be mistaken for no review. The team explicitly says it retained review, while changing how the work is divided between people and machines ¹.

What results does the team report?

The team says that, between June and September 2026, the number of merged pull requests to the Claude Code codebase grew to more than twice its earlier level. Over the same period, it reports that the proportion of changes later reverted fell by more than 40 percent ¹. A pull request is a proposed code change that is considered for inclusion in a project. A revert is a later change that cancels an earlier one.

Those are reported outcomes, not proof of cause. The figures do not show, by themselves, whether machine-based review, changes to the way people review, or other factors produced the difference. The source excerpt does not include the total number of pull requests, the detailed calculation, or a comparison group ¹.

The team also frames its goal around avoiding incidents as well as reversions ¹. The excerpt does not provide an incident count or explain how incidents are defined, so readers cannot use it to assess that part of the claim independently.

What can other developers take from this approach?

The broad lesson is to think about code review as a workflow rather than only as a person reading a patch. Teams can ask which checks might be automated, where human judgment matters, and how they would respond if a change causes trouble. These are practical questions raised by the approach described in the blog; the source does not provide a universal checklist or promise the same results for every team ¹.

Another useful idea is to consider recovery as part of quality control. Reviewing a proposed change can reduce the chance of trouble, while a reliable way to undo a change can limit the disruption if a problem still gets through. The Claude Code team says it invested in making that recovery faster ¹.

The balance will depend on the project. The available account does not say how the team handles sensitive or unusually complex changes, nor does it explain how its process is adjusted for different levels of risk. Developers considering similar methods would need to consult their own team’s standards and requirements.

What are the limits of the team’s account?

The source is a blog post by the Claude Code team, so it is a first-party description rather than an independent assessment ¹. It offers a brief summary of the team’s method and selected results, but not enough detail to reproduce the method or verify every interpretation of the figures.

The blog also says Claude writes about 90 percent of Claude Code’s own code ¹. The supplied material does not explain how the team measured that share. Claude Code is described in the background source as a terminal coding agent developed by Anthropic ³.

For readers, the takeaway is limited but useful: this team says it has adapted review to handle more changes by using machines for much of the reading, retaining human involvement, and making reversals easier. The evidence provided does not establish that this model is suitable for every codebase.