What Is Code Review? (And How to Keep It From Becoming the Bottleneck)

As agentic coding becomes the norm on engineering teams, writing code is rarely the bottleneck in software development anymore. Code review is.

If you're an engineering lead or director, you probably see this firsthand every day:

• Your team is creating code faster than ever, but PR cycle times are getting longer.
• Engineers are spending more time reviewing than coding, and agent code doesn't quite look like something your team would write, even when it passes lint.
• Bugs that review should have stopped are slipping through and ending up in production.

The right code review process can help eliminate these problems, scale your workflows, and help you ship code you can trust, no matter who wrote it. In this article, we'll break down the process and help you find the right tools to streamline code review on your team.

What is code review?

Code review is a peer review of new code during the development process to catch and fix bugs, improve code quality, and enforce coding standards. Code reviews are part of the quality assurance process. They need to fit your development workflow, but generally, they happen at or around the pull request stage.

In a code review, a peer developer reviews new code for style, syntax, logic, and other errors. The review asks not just whether anything is wrong, but also whether the new code and implementation has anything major that should be fixed or improved.

Code reviews help protect source code, improve codebase quality, share knowledge and expertise among team members, and strengthen collaboration.

Traditional code review vs. automated code review

Traditional code review is done by a peer developer, while automated code review is done by a software program.

Traditionally, a peer developer reads the code (and ideally, writes and runs tests) and checks for errors, bugs, or problems. Automated code review uses tools like linters, formatters, and other static analysis (SAST) tools. These tools don't read the code line by line. Instead, they automatically check code for known errors, bugs, and patterns that can cause issues. Since they handle many parts of code review on their own, they free up human developers to review more complex issues.

Automated code review usually checks issues like:

• Style and formatting
• Syntax
• Security vulnerabilities

Many teams split these jobs up among specialized tools. For example, a team might use a formatter to automatically check and fix formatting and style errors, and a linter to catch basic bugs and errors in the code. A SAST tool might then review for security vulnerabilities, and then a developer will review the code for implementation, fixes, and anything missed so far.

Traditional code review vs. AI code review

Traditional code review is done manually by a peer developer, while AI code review uses LLMs to understand the context of your codebase and automatically review code to find (and sometimes fix) bugs and errors.

Unlike automated code review, where a tool scans the code for rule violations and known patterns, an AI code reviewer reviews the code closer to how a human would: looking at the new code as a whole, comparing it with the rest of the codebase, and thinking about how the new code fits in. Instead of just looking at errors in individual lines of code, it can reason about and find errors that only show up in the wider codebase context.

AI code review tools can look for:

• Style and formatting
• Syntax
• Security vulnerabilities
• Logic errors
• Edge cases and (with some tools) runtime errors
• Functionality, performance, and intent

AI code review tools vary widely in scope, noise, relevance, and accuracy. We'll use Greptile as an example, because it's the code review tool we know best:

How the code review process works in 5 simple steps

Code review processes vary by team. Your process should fit cleanly into your workflow. Here's how it generally works:

1. A pull request (or merge request) is opened with new code changes pushed to a branch. This opens a review to suggest or make changes before the new code is merged into the main codebase.
2. Automated checks, like linters or SAST tools, run, catching small errors, cleaning up code, and checking for security vulnerabilities.
3. A human reviewer steps in (or is assigned) to review the new code. All reviewers should use a shared code review checklist to keep reviews and quality consistent. Reviewers leave comments, ask questions, and/or make small fixes to improve the code.
4. The code author reviews the comments and implements any changes.
5. The code is approved and merged.

Code reviews often happen asynchronously, but they can also take place live, with the author walking through the changes and why they made them, and reviewers asking questions and giving feedback. Pair programming is another form of code review.

In 2026, code is no longer the bottleneck

As AI generates code at a much higher volume and speed, code stops being the bottleneck in the software development lifecycle. The bottleneck shifts further down the cycle, to code review. (That's why your PR cycle times are likely still so long, even after automating so much of the coding.)

The problem is that the more code you generate with AI, the more important code review becomes. Studies show that AI-generated code that isn't reviewed well degrades your codebase, and it does so quickly:

• AI-introduced issues survive almost a quarter of the time. Liu et al. tracked AI-introduced issues (code smells, correctness bugs, and security issues) across more than 6,000 GitHub repositories. Out of 464,900 tracked AI-introduced issues, 105,364 still survived in the latest version of the code, a survival rate of 22.7% [1].
• AI code increases code complexity. He et al.'s study of Cursor adoption across 806 GitHub repos found that velocity gains fade within two months of adoption. Meanwhile, code complexity (+41%) and static analysis warnings (+30%) rise at adoption and stay elevated [2].

These issues pile up and erode trust over time, which makes code review crucial in an era of agentic coding.

If you scale code generation and skimp on review, you lose trust and security. But if you scale code generation and keep review manual, all your developers end up spending their time on review. Both are bad options. Review time takes away from shipping, and it pulls your team away from the work where they really count: big ideas, architecture, logic, and development.

The solution is using AI to help with code review. Importantly, you need a code review solution that actually mimics the real engineering process. Good reviewers don't just read the code. They run it. And you need to be able to check the results of an AI review faster than you could review the code yourself.

For example, Greptile's TREX runs PRs in a sandbox and links screenshots, videos, logs, and scripts from its comments, so you can see what actually happened when the code ran instead of reconstructing it from a text-only review. (Of course, you can still review the diff in detail as well.)

Faster code doesn't matter if you're not reviewing it. And reviewing code doesn't matter if you're not reviewing it well.

Poor AI code review offers a false sense of security

"AI code review" is a wide bucket. You'll find plenty of AI code review tools out there that offer little more than a false sense of security.

It's easy to run a fancy tool and assume it's doing what you need. But using AI code review well requires codifying your team's judgment, not just a list of rules. You also need to make sure your AI tools have context for review, understand your entire codebase, and catch bugs accurately for your codebase.

We'd classify "poor" AI code review as any tool that has most of the following flaws:

• Doesn't make contextual suggestions (i.e., relies on rules or the diff alone without indexing your entire codebase)
• Doesn't learn from your team to improve over time
• Only reads code rather than running it
• Uses the same model to review code as the model that wrote it (or is locked into one model, i.e., not model-agnostic)

With only one or two of these flaws, you can find workarounds or use the tool in specific cases with clear guardrails. But poor AI code review tends to produce bloated code, make "fixes" that break other code in the process, and let plenty of bugs through to production.

That's why we designed Greptile as a context-first code review tool. Without context, a code review tool can't make useful suggestions. Greptile indexes and understands your entire codebase, then uses that knowledge to review each new PR. It also learns from your team's actions and feedback to improve over time. More on all that below.

Best AI code review tools by use case

So, what are the best AI code review tools, and which one can actually help your team? Here's how the top AI code review tools stack up:

Jump to a tool:

• Greptile
• Cursor BugBot
• Qodo
• CodeRabbit
• Graphite
• Semgrep
• How the tools compare in benchmarks

Greptile: Best for full-context AI code review

Greptile is an AI code review tool that works the way a senior developer does. Instead of running basic, rule-based static analysis, it learns your entire codebase to provide deep, cross-file, contextual review. It finds bugs other tools (and even humans) miss, and it learns from your team's actions and comments over time, so its reviews stay precise without missing critical errors.

With TREX, it even spins up a sandbox to run your code, catching runtime errors that can't be found by reading a diff. Greptile integrates directly with GitHub, GitLab, Bitbucket, and more to review PRs. This helps teams merge PRs up to 4x faster while catching 3x more bugs.

Best for: Full-context AI code review that catches more bugs by understanding your entire codebase and running your code in a sandbox

Noise level: Low

Cost: Free Starter plan for 1 active developer with 50 credits/month and unlimited repositories; Pro is $30/seat/month; Custom (Enterprise)

Cursor BugBot

BugBot is Cursor's AI reviewer, built to scan pull requests for logic errors, security issues, and edge cases before merge. It favors precision over volume, which keeps noise down but means more bugs get through: in our benchmark, it caught 58% of bugs. And running code generation and review on the same models can lead to model inversion, where the reviewer shares the blind spots of the model that wrote the code.

Best for: Teams already using Cursor who want review and fixes to happen without leaving the editor

Noise level: Medium to low

Cost: Usage-based BugBot billing; included with Cursor Teams at $40/user/month; Custom (Enterprise)

Qodo

Qodo is a governance-first review tool. Teams write and configure rules for their engineering standards and ticket compliance (for example, checking a PR against its Jira ticket), and Qodo enforces those rules on every pull request, using a set of review agents that each check a different area (correctness, standards, risk) and combine their findings into one review. It pays off most when a platform team owns rule configuration and keeps those rules current. Pricing is credit-based: each review draws from a monthly credit pool, and larger or more complex PRs draw more credits.

Best for: Organizations with a dedicated platform team that wants to enforce custom standards and governance policies on every PR

Noise level: Depends on how well the rules are configured and maintained

Cost: Free trial; Pro Team plan from $30/month with credit-based billing; Custom (Enterprise)

CodeRabbit

CodeRabbit is a widely adopted AI code reviewer that generates plain-English PR walkthroughs alongside inline review comments. It surfaces output from language-specific linters and security scanners next to its own comments. Its review emphasis is on the diff, and without active tuning (path filters, learnings, and YAML configuration), it can get noisy on large PRs.

Best for: Fast, diff-level PR summaries

Noise level: Medium to high without tuning

Cost: Free for public repositories; paid plans from $30/user/month billed monthly; Custom (Enterprise)

Graphite

Graphite is built around stacked PRs (breaking large changes into small, dependent PRs that merge in sequence), with AI review woven into that workflow. Its AI review is designed to complement human reviewers rather than replace them, and it keeps comments to a minimum so developers act on what it flags. Graphite was acquired by Cursor in December 2025.

Best for: Teams adopting stacked PRs that want low-noise AI review alongside a human team

Noise level: Low

Cost: Free (Hobby, limited AI reviews); $20/user/month (Starter billed annually); $40/user/month (Team billed annually); Custom (Enterprise)

Semgrep

Semgrep is a security-focused static analysis platform. It offers SAST and SCA scanning with AI-assisted triage and remediation, and it can find vulnerabilities across both known CVEs and more complex business logic flows. It's good for enforcing specific vulnerability, OWASP, or secret-detection rules, but it takes significant rule investment to catch more complex issues.

Best for: Static code analysis with AI capabilities for security scanning

Noise level: Medium, with configuration

Cost: Free for up to 10 contributors; from $30/contributor/month for Code (SAST) or Supply Chain (SCA); Custom (Enterprise)

How do AI code review tools compare?

Different tools take different approaches.

Some tools, like CodeRabbit, comment on more of the diff, which can mean more noise to sort through. Others, like Graphite, keep comments to a minimum, which keeps noise low but lets more bugs through.

Still others, like Greptile, combine several AI approaches to stay precise (low noise) without sending bugs to production. In our benchmark of 50 real bugs from five open-source repositories, run on default settings, Greptile caught 82% of them.

Here's a quick look at how some of these tools stack up:

Need more options and comparisons? Check out these 27 code quality tools that catch bugs.

How to choose a code review tool and what to look for

There's no one right code review tool for every team. The key is to look at the dimensions that matter for your team, context, and codebase. When evaluating code review tools, look for:

• Type of review. Do you need a basic linter, or something more complex? Automated code review tools can help with the basics, but AI code review tools give you deeper, context-aware suggestions and catch more bugs. AI code review tools also work and perform very differently: if you've tried one, you haven't tried them all.
• Context. Code review is essentially a game of finding the most important context. Look for a tool that builds and references the context of your entire codebase (i.e., indexes and understands it, like Greptile does) and knows how to find the context that matters, so it catches important bugs without piling on nits.
• Signal-to-noise ratio. A noisy code review tool will annoy your engineering team, and devs will start ignoring its comments altogether. Look for a tool that comments when it matters and is precise without missing real bugs.
• Accuracy. Look for tools that catch most bugs without a high false-positive rate. You can also look at what kinds of bugs the tool can catch (security vulnerabilities? Logic errors?) and what percentage of the time it catches them.
• Complexity and team size. Look for tools that are designed for your situation. Can the tool handle your codebase's complexity and setup? Does it fit your team size? Some tools work best in one area (e.g., many code review tools work best for smaller teams with simple codebases), while others offer broader coverage. Greptile is built for large teams running complex codebases, and it also works well for medium-sized and smaller teams.
• Workflow and stack support. Finally, your code review tool should fit into your existing process. Look for tools that fit your repository setup and PR process, support your core languages, and work anywhere you do.

For a more thorough walkthrough of what to look for and how to run an eval, check out our guide to evaluating code review tools for your team.

How Greptile's full-context AI code review helps teams catch real bugs before they merge

The key to future-proofing your code review process is finding an AI code review solution that can scale your review volume without sacrificing trust and understanding. To do that, you need a code review solution that mimics the real engineering process.

That's why we built Greptile: an AI code reviewer that reviews your code like a senior dev would. It builds and understands your full codebase context, reviews with that in mind, learns from your team to improve, and runs your PRs in a sandbox to catch runtime errors that can't be found by just reading a diff.

Most importantly, it knows how to find and focus on high-priority issues while skipping nits, based on your codebase context:

Greptile is designed for teams that other code review tools don't work for:

• Large enterprise engineering teams with hundreds of engineers working on complex monorepos
• Small teams moving fast with growing PR volume that don't want to be slowed down by code review
• Teams working on high-security, high-risk codebases that need code review they can trust

With Greptile, you can scale beyond traditional and automated code review processes while avoiding common AI code review problems. Here's what makes that possible:

• Greptile Agent: Greptile indexes your entire codebase and reviews each PR against that context, catching bugs in the seams between files, services, and shared dependencies, not just issues visible in the diff.
• TREX: Once enabled, TREX spins up a sandbox for the PRs that match your filters, runs your code, and links screenshots, scripts, logs, and comments showing what broke and why. This way, you can find runtime errors that can't be found by reading a diff and fix them before they go to production.
• Learning and custom context: Greptile maps your codebase and learns from your team over time, improving its comments, suggestions, and bug catches based on your team's responses, feedback, and actions. It reads the new code and understands how it fits with and affects the rest of your codebase.
• Greplooping: Our "greploop" skill lets Greptile review agent-written code, then iterate with the agent on finding and fixing bugs until the PR receives a 5/5 confidence score. More autonomy, and less time senior developers spend on minor bugs and patches.
• Review code with third-party context: When your code touches a partner API, Greptile reviews it against documentation, best practices, and common failure points maintained by the partner.
• Security: Greptile runs a security scan on every PR, pairing rule-based scanning and SCA with AI-based security review to find more vulnerabilities, separate what actually matters, and catch problems other tools miss. Self-hosting and enterprise-grade security features mean running Greptile doesn't require sacrificing your security standards.
• Independent, model-agnostic validation: Greptile is the independent validation layer for human-written and AI-generated code. Whether a PR comes from a developer, Claude Code, Codex, Cursor, Devin, or another agent, Greptile applies the same full-codebase review before merge. It uses a combination of frontier models from OpenAI and Anthropic to draw on their respective strengths, and it works with any coding agent you use.

That's how teams like NVIDIA, Podium, and Vouch are cutting merge times, shipping faster, and catching more bugs:

• One NVIDIA team cut its average time to merge by 75%, from more than 24 hours to six, and Greptile has reviewed 395,000+ pull requests across NVIDIA's repos. "Code reviews happen very quickly because Greptile is the first code reviewer. We're spending a lot less time doing code reviews, and are much more productive in shipping code." (Todd Tanber, Senior Engineering Manager, Diagnostics, Architecture and Infrastructure, NVIDIA)
• Vouch has caught 2,000+ critical issues with Greptile while reducing review time by 85%. "We get comments like 'This package exposes an API that looks like this, but you are using it incorrectly', which is very useful. Static analysis tools would not be able to help in that situation." (Dan Goslen, Senior Software Engineer III, Vouch)
• Podium reviews 8,400+ code changes per week faster and more effectively with Greptile. "Greptile frequently exposes missed items during code reviews. This has increased our deployment and code quality delivered in general." (Emmanuel Pinault, Software Architect, Podium)

What can Greptile do for your team? Try it free today and see for yourself →

FAQs about AI code review tools

What is the most accurate code review tool?

The most accurate code review tools have full codebase context, learn from your team and codebase, and can be customized to your team's styles, rules, and requirements. In our benchmark of 50 real bugs from open-source pull requests, Greptile led with an 82% bug catch rate.

Can I use Claude Code for code review?

Yes. Claude Code offers two ways to review code. The first is the Claude Code CLI, where you can run one-off reviews manually. The second is Claude Code Review, a managed PR reviewer that Anthropic launched in March 2026 as a research preview for Team and Enterprise customers. It automatically reviews pull requests on GitHub when they open, and it's billed on token usage, averaging $15 to $25 per review.

Greptile's edge is flexibility with predictable pricing. Every review draws from a set number of credits, so you know what a review costs before it runs. A standard review uses 1 credit. Greptile Plus and Apex are deeper reviews that spend more time on each PR and catch more bugs: Plus uses 3 credits and Apex, built for large, complex PRs, uses 10. Pro includes 50 credits per seat each month, with extra credits at $1 each. You can pick the review depth per PR, or set rules to use Plus or Apex automatically for large PRs or high-risk code.

Can I use Codex for code review?

Yes. OpenAI's Codex can review a diff from the terminal, and its GitHub integration can review pull requests on request or automatically. Like Claude Code Review, it's tied to one model provider's plans and usage. Greptile reviews with full-codebase context across GitHub, GitLab, and other platforms, and it stays independent of the tool that wrote the code: the same review applies whether a PR comes from Codex, Claude Code, Cursor, or a person.

How does Greptile compare to CodeRabbit?

Both CodeRabbit and Greptile review pull requests with AI-generated comments. The difference is review depth: Greptile applies full-codebase context to each PR, while CodeRabbit's review emphasis is on diff-level annotation and PR walkthroughs. In a head-to-head benchmark on 50 open-source PRs, Greptile caught over 50% more bugs than CodeRabbit. See the full Greptile vs. CodeRabbit comparison for benchmark results and side-by-side review examples.

How does Greptile compare to Cursor BugBot?

BugBot is built around Cursor's editor environment and fits best for teams whose developers already write and fix code in Cursor. Greptile is editor-agnostic and reviews PRs from any developer or coding agent (Claude Code, Codex, Cursor, Devin, or a human author) using full-codebase context that BugBot's diff-focused review does not include. Teams using multiple editors or wanting a standalone validation layer pick Greptile. See the full Greptile vs. BugBot comparison for benchmark results and side-by-side examples.

How does Greptile compare to Qodo?

Qodo is governance-first: teams that invest in configuring its rules get reviews that enforce specific engineering standards and ticket compliance, billed through a credit pool where larger PRs draw more credits. The trade-off is upfront and ongoing rule configuration. Greptile catches codebase-specific bugs out of the box using full-codebase context, with optional plain-English custom rules in a .greptile/ config folder for teams that want directory-scoped standards. Qodo suits organizations with a dedicated platform team to own rule maintenance; Greptile suits teams that want bug-catching review without that overhead.

Is Greptile free?

Yes. Greptile offers a free Starter plan for one active developer with 50 credits per month and unlimited repositories. Greptile is also free for qualified open-source projects. Pro is $30/seat/month with 50 credits per seat per month, extra credits at $1 each, and custom review rules included.

Does Greptile work for small teams and startups?

Yes. Most startups and small teams build on interconnected services and external APIs, which is exactly where diff-only review misses bugs. Greptile offers a free Starter plan for one active developer with 50 credits per month and unlimited repositories, plus 50% off for pre-Series A startups. Greptile installs as a GitHub or GitLab app with reviews live in around five minutes, with no YAML configuration required.

Sources

[1] Liu, Y., Widyasari, R., Zhao, Y., Irsan, I.C., Chen, J., and Lo, D. "Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild." arXiv:2603.28592, March 2026. arxiv.org/abs/2603.28592

[2] He, H., Miller, C., Agarwal, S., Kästner, C., and Vasilescu, B. "Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects." Mining Software Repositories (MSR '26), April 2026. cmustrudel.github.io/papers/msr2026he.pdf