Anthropic's New Code Review Tool: How to Build a Generate-Then-Review Workflow in Beam

March 2026 • 8 min read

On March 9, 2026, Anthropic shipped a feature that acknowledges a truth the industry has been dancing around: when AI writes most of your code, you need AI to review it too. The new Code Review tool inside Claude Code is purpose-built to catch the bugs, security holes, and subtle anti-patterns that slip through AI-generated pull requests. With Claude Code's run-rate revenue now surpassing $2.5 billion, this is not an experimental feature -- it is Anthropic's answer to the quality crisis created by the scale of AI-assisted development.

This guide covers what the Code Review tool does, how it works under the hood, and -- most importantly -- how to set up a practical generate-then-review workflow using Beam's split panes so that every line of AI-written code gets a second pair of AI eyes before it hits your repository.

Why AI-Generated Code Needs AI Code Review

The volume problem is real. Developers using Claude Code, Cursor, Copilot, and similar tools are merging more code per day than any human reviewer can meaningfully inspect. A team of five engineers might produce 30-50 pull requests daily when each engineer has an AI coding agent running alongside them. Traditional code review -- where a human reads every diff line by line -- cannot scale to that throughput.

The failure modes of AI-generated code are also different from human-written code. AI does not make typos or forget semicolons. Instead, it introduces subtler problems: insecure default configurations, race conditions in concurrent code, overly broad exception handling that swallows errors silently, and dependency choices that introduce supply chain risk. These are exactly the kinds of issues that a focused review tool can catch systematically.

Human code review catches the mistakes humans make. AI code review catches the mistakes AI makes. They are different categories of error, and you need both.

What Anthropic's Code Review Tool Does

The Code Review tool runs inside Claude Code as a dedicated review mode. Rather than generating new code, it analyzes existing code -- whether that code was written by a human, by Claude itself, or by another AI agent. It examines diffs, entire files, or full repositories and produces structured feedback across several dimensions.

You trigger it by running claude review in your terminal. It accepts flags for scope -- --diff to review only staged changes, --file path/to/file.ts to review a specific file, or --pr 42 to review an entire pull request. The output is a structured report with severity levels (critical, warning, suggestion) and inline references to specific lines.

The Code Review Pipeline

Here is how the review process flows from code generation to merged pull request. Each stage feeds into the next, and the key insight is that the review step should happen before the PR is even opened -- not after.

Setting Up the Generate-Then-Review Workflow

The most effective way to use Code Review is not as a final check before merging -- it is as a continuous feedback loop during development. You write code in one terminal and review it in another, iterating until the review comes back clean. Here is the concrete setup.

Step 1: Open Two Terminals in Beam

Launch Beam and create a split pane with Cmd+D (or Ctrl+D on Windows/Linux). Your left pane is for code generation. Your right pane is for code review. Both should be in the same project directory.

Step 2: Generate Code in the Left Pane

# Left pane — start Claude Code in interactive mode
claude

# Give it a task
> Add input validation to the user registration endpoint.
  Validate email format, password strength (min 12 chars,
  one uppercase, one number, one special char), and
  sanitize the username field against XSS.

Claude Code generates the implementation. It creates or modifies files, stages changes, and shows you the diff. So far this is the standard Claude Code workflow. The difference is what happens next.

Step 3: Review in the Right Pane

# Right pane — review the staged changes
claude review --diff

# Or review a specific file
claude review --file src/middleware/validation.ts

# Or review with extra context about your project's conventions
claude review --diff --context "We use zod for validation schemas
and never throw raw Error objects"

The review output appears in your right pane while the left pane remains in its Claude Code session. You can read the findings without switching context. If the review flags issues, switch back to the left pane and tell Claude Code to fix them.

Step 4: Iterate Until Clean

# Left pane — address the review findings
> The review found that the password regex doesn't account
  for unicode characters and the email validation doesn't
  check MX records. Fix both issues.

# Right pane — re-review after fixes
claude review --diff

What the Review Tool Catches That You Will Miss

After running the Code Review tool across several production codebases, patterns emerge in what it consistently flags that human reviewers overlook.

Advanced: Automating the Review in CI

The split-pane workflow is ideal during development. For team-wide enforcement, you can add the Code Review tool to your CI pipeline so every pull request is automatically reviewed before a human even looks at it.

# .github/workflows/ai-review.yml
name: AI Code Review
on: [pull_request]
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - name: Run Claude Code Review
        run: |
          claude review --pr ${{ github.event.pull_request.number }} \
            --format github \
            --severity warning \
            --output review-results.json
      - name: Post Review Comments
        if: always()
        run: |
          claude review --post-comments review-results.json

The --format github flag outputs findings as GitHub review comments, posted directly on the PR. The --severity warning flag sets the minimum severity threshold -- anything below warning is suppressed. This prevents noisy suggestion-level comments from cluttering every PR.

Why Side-by-Side Matters

You could run generation and review in the same terminal sequentially. But the side-by-side layout in Beam changes the workflow in a meaningful way: the review findings stay visible while you fix the code. You do not have to scroll back through terminal history to remember what the review said. You do not lose context switching between windows.

This matters because code review is fundamentally a reference task. You read the finding, then you look at the code, then you decide how to fix it. If the finding disappears when you switch to the code editor, you are relying on memory. With split panes, the finding stays on screen while you address it in the adjacent pane.

Beam's project system adds another layer. When you assign both terminals to the same project, their working directories stay synchronized. Your review terminal is always looking at the same codebase as your generation terminal. Switch projects, and both panes update together.

The Bigger Picture: AI Writing and Reviewing Code

Anthropic releasing Code Review alongside Claude Code's massive revenue growth signals where the industry is heading. The code generation market solved the speed problem -- developers can produce code faster than ever. The next bottleneck is quality assurance at that speed. You cannot hire enough human reviewers to keep up with AI-assisted output.

The generate-then-review pattern is becoming the standard workflow for professional development teams. One AI agent writes. Another reviews. A human makes the final call. This is not replacing human judgment -- it is augmenting it by ensuring that when a human does review a PR, the obvious issues have already been caught and fixed.

Setting up this workflow takes five minutes. The split-pane layout in Beam makes it ergonomic. And the alternative -- shipping unreviewed AI-generated code to production -- is a risk that no serious team can afford.