# code review

Plannotator is a local, browser-based review surface for AI coding agents such as Claude Code, Codex, Copilot CLI and Gemini CLI. When an agent proposes a decision, plan or HTML artifact, the app opens in the browser so the user can annotate, comment and send feedback back to the agent; the new Plannotator Inbox gathers messages, questions and files from several agents into one local window, and replies do not require keeping a session open.

Plannotator is a local, browser-based review surface for AI coding agents such as Claude Code, Codex, Copilot CLI and Gemini CLI. When an agent proposes a decision, plan or HTML artifact, the app opens in the browser so the user can annotate, comment and send feedback back to the agent; the new Plannotator Inbox gathers messages, questions and files from several agents into one local window, and replies do not require keeping a session open.

Alibaba has open-sourced Open Code Review, its internal AI code-review assistant used for two years, and a user only needs to configure a model endpoint to start. It reads Git diffs and sends changed files to a configurable LLM through an agent with tool use, which can read full files, search the codebase and consult other changed files to produce structured, line-level review comments. On a benchmark of 50 open-source repositories, 200 real pull requests and 10 programming languages, it reaches clearly higher precision and F1 than a general-purpose agent with the same underlying model while consuming about one-ninth the tokens, but its recall is lower, a deliberate trade-off favoring precision over noise.

Alibaba has open-sourced Open Code Review, its internal AI code-review assistant used for two years, and a user only needs to configure a model endpoint to start. It reads Git diffs and sends changed files to a configurable LLM through an agent with tool use, which can read full files, search the codebase and consult other changed files to produce structured, line-level review comments. On a benchmark of 50 open-source repositories, 200 real pull requests and 10 programming languages, it reaches clearly higher precision and F1 than a general-purpose agent with the same underlying model while consuming about one-ninth the tokens, but its recall is lower, a deliberate trade-off favoring precision over noise.

Anthropic's Claude Code team moved most code reading to automated review and kept humans for high-risk decisions. From June to September, merged pull requests to the Claude Code codebase more than doubled while the share that had to be reverted fell by more than 40 percent. Claude verifies changes before submission with runnable tests or builds, every pull request gets a multi-agent bug review with voting filters, and risk tiers set how many human approvals a change needs.

Anthropic's Claude Code team moved most code reading to automated review and kept humans for high-risk decisions. From June to September, merged pull requests to the Claude Code codebase more than doubled while the share that had to be reverted fell by more than 40 percent. Claude verifies changes before submission with runnable tests or builds, every pull request gets a multi-agent bug review with voting filters, and risk tiers set how many human approvals a change needs.

CRBugBench tests code review agents against 1,800 human-fixed bugs: the best configuration catches 79.6% of pre-merge bugs within its top-5 comments, but performance drops noticeably on proprietary industry repositories.

CRBugBench tests code review agents against 1,800 human-fixed bugs: the best configuration catches 79.6% of pre-merge bugs within its top-5 comments, but performance drops noticeably on proprietary industry repositories.

Tessl's engineering team runs a software factory on one monorepo, producing over 1,200 agent-written pull requests a week, and review had become its largest variable cost. The team encoded its own coding conventions, design system and AI code review policy into Tessl Code Review and set the review policy itself, so about 75% of PRs merged without a human correcting the agent or discussing the engineering. In a one-week side-by-side run over more than 600 PRs, 91% of its findings were real defects against 68% for the previous AI reviewer, with 1.56 versus 0.75 author-fixed bugs per PR and about 35% lower review cost per PR.

Tessl's engineering team runs a software factory on one monorepo, producing over 1,200 agent-written pull requests a week, and review had become its largest variable cost. The team encoded its own coding conventions, design system and AI code review policy into Tessl Code Review and set the review policy itself, so about 75% of PRs merged without a human correcting the agent or discussing the engineering. In a one-week side-by-side run over more than 600 PRs, 91% of its findings were real defects against 68% for the previous AI reviewer, with 1.56 versus 0.75 author-fixed bugs per PR and about 35% lower review cost per PR.

Anthropic has moved Code Review out of research preview into general availability for Claude Team and Enterprise organizations. Coding agents keep raising the pace of code writing while review remains the bottleneck; preview users said it caught real issues before a human reviewer saw the PR but wanted quieter, change-sized, faster reviews. General availability cuts most reviews from about 20 minutes to 6-10 minutes and lets admins pick Quick, Standard or Deep per repository.

Anthropic has moved Code Review out of research preview into general availability for Claude Team and Enterprise organizations. Coding agents keep raising the pace of code writing while review remains the bottleneck; preview users said it caught real issues before a human reviewer saw the PR but wanted quieter, change-sized, faster reviews. General availability cuts most reviews from about 20 minutes to 6-10 minutes and lets admins pick Quick, Standard or Deep per repository.