AI-Enhanced Code Reviews in Cursor

It is Friday afternoon. You have six pull requests waiting for review. Two are from a junior developer who tends to miss error handling. One is a 47-file refactor from a senior engineer that will take an hour just to understand the scope. Another touches the payment flow and needs careful security scrutiny. Your team’s velocity depends on fast review turnaround, but rubber-stamping PRs leads to production incidents. You need a way to review thoroughly without making it your entire afternoon.

Cursor offers three distinct mechanisms for code review: Agent Review in the IDE, BugBot for automated PR analysis, and the Cursor CLI for custom review workflows in GitHub Actions. Each serves a different part of the review pipeline, and combining them gives you coverage that no single approach can match.

What You’ll Walk Away With

• A workflow for using Agent Review to pre-screen your own changes before pushing
• BugBot configuration that catches real issues without generating noise
• A CLI-based custom review workflow for team-specific standards
• Copy-paste prompts for reviewing complex PRs, security-sensitive code, and large refactors
• Project rules that encode your team’s review checklist so the AI enforces it automatically

Agent Review: Pre-Flight for Your Own Code

Before you push a commit, Agent Review lets you run Cursor’s analysis against your local diff. This is the fastest way to catch issues before they reach a PR.

After Agent generates changes (or after you have uncommitted changes), click Review in the diff view, then Find Issues. The agent analyzes your changes line by line and flags potential bugs, logic errors, and missing edge cases.

You can also review all changes against your main branch from the Source Control tab. This is particularly useful before opening a PR — it gives you a pre-flight checklist of issues to address.

Configuring Agent Review

In Cursor settings, you can enable:

• Auto-run on commit: Automatically scans for bugs after each commit
• Include submodules: Reviews changes in Git submodules
• Include untracked files: Catches issues in files not yet staged

Enable auto-run on commit if you want continuous review feedback. Disable it if you prefer to trigger reviews manually to save on usage.

Reviewing Specific Concerns

Sometimes you want the review focused on a particular concern. Use the Agent chat alongside the diff:

Tip

Copy-paste prompt for security-focused review of your changes:

Review my current git diff for security vulnerabilities:

1. Check for SQL injection, XSS, and CSRF vulnerabilities
2. Verify that user input is validated and sanitized
3. Ensure secrets are not hardcoded or logged
4. Check that authentication and authorization are properly enforced
5. Look for unsafe deserialization or eval usage
6. Verify error messages don't leak internal details

Only flag issues in the changed lines, not pre-existing code.

BugBot: Automated PR Reviews

BugBot is Cursor’s managed service for automated pull request reviews. Enable it in your Cursor dashboard (Settings > BugBot), connect your GitHub repository, and it will automatically review every PR.

BugBot works differently from a manual review prompt. It uses specialized analysis tuned for catching bugs in diffs — not just style issues, but actual logic errors, missing edge cases, and potential regressions.

Customizing BugBot with Rules

The real power of BugBot comes from combining it with project rules. Create a .cursor/rules/bugbot.md file with your team’s specific review criteria:

.cursor/rules/bugbot.md

# Code Review Standards

## Always Check
- Every new API endpoint must have input validation using Zod
- Database queries must use parameterized inputs, never string concatenation
- Error handling must not swallow errors silently (no empty catch blocks)
- New React components must have TypeScript prop interfaces, not inline types
- Async functions must have try/catch or .catch() error handling

## Performance
- No N+1 queries: if a loop contains a database call, flag it
- No synchronous file system operations in request handlers
- Images must use lazy loading in frontend code

## Testing
- New utility functions must have corresponding test files
- Test files must include at least one edge case test
- Mock external services, never hit real APIs in tests

BugBot consults these rules during review, so its feedback aligns with your team’s actual standards rather than generic best practices.

Note

BugBot rules use the same project rules system as Agent mode. Rules with “Always Apply” scope are included in every BugBot review. Use glob patterns to scope rules to specific file types — for example, apply stricter security rules to files matching src/api/**/*.ts.

CLI-Based Custom Review Workflows

For teams that need review logic beyond what BugBot provides — compliance checks, architectural pattern enforcement, or integration with external tools — the Cursor CLI enables fully custom review workflows in GitHub Actions.

Tip

Copy-paste prompt for creating a custom review GitHub Action:

Create a GitHub Actions workflow at .github/workflows/code-review.yml that:

1. Triggers on pull_request events (opened, synchronize, reopened)
2. Installs Cursor CLI via curl https://cursor.com/install
3. Runs an agent that reviews the PR diff using gh pr diff
4. Flags only high-severity issues (security, logic errors, data loss risks)
5. Posts inline comments on specific changed lines
6. Adds a summary comment at the end
7. Does NOT approve or request changes -- only comments
8. Limits to 10 inline comments maximum per review

Use .cursor/cli.json to deny Write and git push permissions.
Use the CURSOR_API_KEY secret for authentication.

The permissions configuration ensures the review agent can read code and post comments but cannot modify your repository:

.cursor/cli.json

{
  "permissions": {
    "deny": [
      "Shell(git push)",
      "Shell(gh pr create)",
      "Shell(gh pr merge)",
      "Write(**)"
    ]
  }
}

Reviewing Large PRs

Large PRs are where AI review delivers the most value. A human reviewer’s attention degrades after the first 200 lines of diff. The AI maintains the same level of scrutiny across 2,000 lines.

For large refactors, break the review into focused passes:

@src/services

This PR refactors the service layer from class-based services to functional modules.
Review the changes in three passes:

Pass 1 - Correctness: Do the new functional implementations preserve the same
behavior as the class-based versions? Check return types, error handling, and
edge cases.

Pass 2 - API Surface: Are the exported function signatures backward-compatible?
Will consumers of these services need to update their imports or call patterns?

Pass 3 - Testing: Do the existing tests still make sense with the new
implementation? Are there new code paths that lack test coverage?

Summarize findings for each pass separately.

This structured approach prevents the AI from mixing up concerns and gives you a clear checklist to work through.

Reviewing for Specific Patterns

Sometimes you need the review to enforce a specific architectural pattern rather than general best practices:

Tip

Copy-paste prompt for architecture-focused review:

Review this PR against our architecture rules:

1. API routes must not contain business logic -- they delegate to service functions
2. Service functions must not import from route files (no circular dependencies)
3. Database access goes through the repository layer, never called directly from services
4. Cross-service communication goes through the event bus, not direct function calls
5. React components must not call API endpoints directly -- use custom hooks

For each violation, show the file, line number, and a suggested fix.

Building a Review Culture with AI

The most effective teams use AI review as a first pass, not a replacement for human review. Here is a workflow that works:

1. Author runs Agent Review locally before pushing. Fix obvious issues before the PR is even created.
2. BugBot reviews the PR automatically when it is opened. It catches bugs, missing tests, and security issues.
3. Human reviewer focuses on architecture, design decisions, and business logic — the things the AI is less equipped to judge.
4. Author addresses feedback from both AI and human reviewers. For AI feedback, they can ask Agent mode to implement the fix directly.

This pipeline means human reviewers spend their time on high-value feedback instead of catching typos, missing null checks, or inconsistent error handling.

When This Breaks

BugBot generates too many false positives. This usually means your project rules are too broad or contradictory. Narrow them down: instead of “all functions must have error handling,” say “async functions in src/api/ must wrap their body in try/catch.” Specific rules produce specific feedback.

Agent Review misses obvious bugs. The review is only as good as the model and context available. For complex logic, ask directly: “Is there a case where this function returns undefined instead of throwing?” Targeted questions get better answers than open-ended “review this code.”

CLI review workflow posts duplicate comments. If your workflow triggers on synchronize events (new commits pushed to the PR), it reviews the entire diff again. Include logic in your agent prompt to check for existing comments: “Before posting a comment, check if similar feedback already exists on nearby lines using gh pr view —json comments.”

The review takes too long and blocks the PR. Set a timeout on your GitHub Actions job (10 minutes is usually sufficient). If the review consistently times out, reduce the scope: review only changed files, not the entire codebase.

What’s Next

Pair Programming Use Cursor as a real-time collaborator, not just a reviewer.

CI/CD Pipelines Integrate your review workflow into a complete CI/CD pipeline.

Testing & TDD Generate comprehensive tests alongside your review workflow.