Table of contents

• What is AI code review?
• How AI code reviewers work
• Why AI code reviewers matter
• Featured tool: Diamond by Graphite
• Other tools in the ecosystem
• Conclusion

What is AI code review?

AI code review uses artificial intelligence to automatically analyze and review source code. Unlike traditional linters or static analyzers that rely on rigid rule sets, AI code reviewers leverage machine learning models trained on vast amounts of code to recognize patterns, catch bugs, and make recommendations.

For developers, this means getting intelligent, real-time feedback on potential logic errors, performance bottlenecks, or even security issues—as soon as code is written or submitted for review.

This form of automated code review is increasingly common in modern software development, where codebases are large, teams are distributed, and review speed matters. For a broader overview, see our guide on "What is AI code review?"

How AI code reviewers work

AI code reviewers typically combine traditional static analysis with machine learning techniques:

1. Parsing and rule-based checks: Like linters, AI tools parse code to build an abstract syntax tree and apply base rules to catch obvious issues (e.g., undefined variables, syntax errors).

2. Machine learning evaluation: ML models compare submitted code to patterns learned from millions of open-source and enterprise projects. This helps the system flag non-obvious issues like inefficient loops, incorrect conditions, or risky dependencies.

3. Context awareness: Advanced reviewers don't just look at a diff in isolation. They evaluate the surrounding codebase to understand the full impact of a change—including cross-file dependencies and style consistency.

4. Actionable feedback: Suggestions are provided in natural language, often directly on the pull request. Many tools include one-click fixes for issues like duplicate code, unsafe operations, or missing test coverage.

5. Continuous learning: These systems improve over time by ingesting more code and incorporating developer feedback.

Why AI code reviewers matter

For developers and teams, AI code reviewers offer practical, everyday benefits:

• Catch bugs early: AI reviewers find issues before they reach production, reducing bugs and post-release firefighting.
• Speed up reviews: Tools provide near-instant feedback, helping developers fix problems before human review even begins.
• Maintain consistency: AI enforces coding standards uniformly across large teams and codebases.
• Educate developers: Suggestions often explain why a fix is needed, making them great learning tools for junior devs.
• Reduce technical debt: Frequent, automatic review prevents the buildup of low-quality or inconsistent code.

Featured tool: Diamond by Graphite

Graphite offers an AI-powered code review agent called Diamond. Diamond integrates directly into GitHub pull requests and provides contextual, intelligent feedback almost instantly. For a comparison of top tools, see Best AI code review tools of 2024.

Here's how Diamond stands out:

• Codebase-aware: Diamond doesn't just analyze the change – it understands the entire repo, reducing false positives and irrelevant suggestions.
• Fast and actionable: Comments appear seconds after a PR is opened, giving developers real-time, high-signal feedback.
• Practical examples: For instance, Diamond can detect issues like using the same React ref for multiple DOM nodes (which breaks the IntersectionObserver API) and offer a code fix in one click.
• Team-friendly: Diamond supports custom rules and scales across teams, enforcing shared practices without needing every senior engineer to review every line.

Diamond is particularly useful for fast-moving engineering teams that want to keep review quality high without slowing down deployment velocity.

Other tools in the ecosystem

While Diamond is gaining traction, it's not the only AI code review tool available. For best practices on implementing these tools, see AI code review implementation and best practices.

• Amazon CodeGuru Reviewer: Designed for Java and Python, it identifies performance issues, resource leaks, and concurrency bugs.
• DeepCode by Snyk: Focuses on security vulnerabilities and code quality by scanning for known bad patterns in real time.
• Codacy: Offers automated code review with customizable quality rules, mostly for style and performance.
• Greptile: An AI-powered PR Reviewer that understands your entire codebase, provides context‑aware inline comments, quick‑fix suggestions, and natural‑language PR summaries.
• Ellipsis: An AI teammate installed via GitHub that reviews code on every commit, auto‑fixes bugs, enforces style guides, answers questions, and even generates changelogs.

These tools reflect a broader trend: code review is no longer a fully manual task. AI is helping bridge the gap between fast iteration and code reliability.

Conclusion

AI code reviewers are transforming code review from a manual bottleneck into a fast, consistent, and educational process. Tools like Diamond demonstrate how machine learning and intelligent analysis can catch bugs, improve team velocity, and elevate code quality.

Whether you're a solo developer or part of a large team, trying out an AI-powered code review tool can improve your workflow. Automated code review helps you focus on logic and architecture, while the AI handles the low-hanging issues—quickly, reliably, and at scale.