AI reviewers run where developers already work. Findings show up as PR comments, as CI/CD checks that pass or fail, and sometimes in the IDE before a PR exists. They review code from humans and coding agents alike.
You'll also see the term "agentic code review." It describes a reviewer that takes multiple steps and uses tools instead of commenting once on a diff, and it gets its own section below.
AI code review means AI acting as the reviewer. That's a different problem from people reviewing what a coding agent produced. If you're looking for the second one, read our guide on reviewing code an agent wrote.
This guide covers how AI code review works, what it checks, and how it compares with the linters and SAST tools you already run. It also defines agentic code review, names the places these reviewers fall short, and explains why security is the area where AI review pays off most.
How does AI code review work?
AI code review works by collecting context around a code change, analyzing it with LLM reasoning, deterministic analysis, or both, and posting findings for a person to act on. The workflow looks similar across most tools:
- A trigger starts the review. Opening a pull request, pushing a new commit, or a manual request kicks it off.
- The reviewer collects context. At minimum it reads the diff. Better tools also pull in surrounding files, related functions, and repo instruction files that describe team conventions.
- The reviewer analyzes the change. Depending on the tool, that means LLM reasoning, deterministic static analysis, or both.
- Findings get posted. Each comment usually carries a severity, an explanation, and often a suggested fix the developer can apply.
- A person decides. The author fixes, dismisses, or discusses each finding, and a reviewer owns the merge.
The analysis step is where tools differ most. Two kinds of analysis tend to show up.
Deterministic analysis gives the same answer every time for the same input. Pattern matching, dataflow tracing, and type checks fall into this group. Its results are repeatable and auditable, but it only finds what someone wrote a rule for.
LLM reasoning reads code the way a person does. It can infer intent, notice that a new endpoint skips an authorization check its sibling endpoints perform, or flag a function whose name promises something its body doesn't do. It's also probabilistic, so running it twice on the same diff can produce different comments.
Most serious tools combine the two. Deterministic analysis anchors findings in facts about the code, and LLM reasoning covers what rules can't express. Endor Labs AI SAST is one example of this hybrid model. It orchestrates multiple specialized AI agents that combine rule-based pattern matching, dataflow tracing, and multi-pass LLM reasoning. The deterministic layers establish where data flows, and the LLM passes reason about whether that flow is a problem.
What does AI code review check?
AI code review checks a change for correctness, quality and maintainability, and security. Each one catches a different kind of mistake.
Correctness
These are the bugs that make code do the wrong thing. An AI reviewer might catch an off-by-one error in a pagination loop or a null value that reaches a method expecting an object. It can also spot an edge case the tests don't cover, like an empty list or a negative quantity. Missed updates are a strong area too: a developer renames a field in one class and forgets the serializer that depends on it. Some reviewers also point out that a new code path has no tests.
Quality and maintainability
Here the reviewer flags duplicated logic, functions that have grown too complex, unclear names, and drift from team conventions. A typical comment says something like "this block repeats the retry logic in http_client.py; consider reusing it." Repo instruction files matter most in this category, because good style depends on the team.
Security
AI reviewers can flag injection, authentication and authorization flaws, hardcoded secrets, and risky dependency changes when the tool integrates with software composition analysis (SCA). Security is also where the gap between tools is widest, which is why it gets its own section below.
How is AI code review different from linters, SAST, and human review?
AI code review adds a layer that reasons about intent, and it doesn't remove the need for linters, SAST, or human review. Most teams already run linters and some form of static application security testing (SAST), so the real question is where AI review fits next to them.
Linters enforce style and simple rules line by line. They're fast and predictable, and they don't understand what the code is for.
Rule-based SAST matches known vulnerability patterns and traces tainted data from source to sink. That works well for well-described bug classes. The bug classes that do the most damage often have no pattern to match, though. Broken access control, insecure direct object references (IDOR), insecure design, and business logic flaws depend on what the code is supposed to do. Injection also slips through when the data path crosses files and functions in ways a rule doesn't model.
AI code review reasons about intent. It can notice that a handler returns a record without checking that the caller owns it, because it compares the change with how similar handlers behave.
Human review still owns intent, architecture, threat modeling, and sign-off. AI review changes what the reviewer sees when they open the PR. Instead of 40 nitpicks and a buried authorization bug, they start from triaged findings and spend their attention on design. The OWASP Code Review Guide has long recommended pairing automated tools with manual review focused on the riskiest parts of an application.
What is agentic code review?
Agentic code review is AI code review performed by agents that plan and take multiple steps with tools, instead of commenting once on a diff.
A single-pass reviewer reads the diff, maybe a few surrounding lines, and writes comments. That's fast and useful for local issues, but it misses anything that depends on code outside the diff.
An agentic reviewer works more like a security engineer with a terminal. It can:
- Pull repo context beyond the diff, such as callers of a changed function or the middleware that's supposed to enforce authentication
- Call tools, including static analyzers, dependency scanners, and secrets detectors
- Trace data across files and functions to see where user input ends up
- Classify its own findings before posting them
- Propose a fix that matches the codebase's conventions
Endor Labs AI Security Review splits this work across specialized agents. Detection agents read a change the way a security engineer would, tracing how data moves across files and functions. A triage agent classifies each finding as a true positive, a false positive, or unknown, with reasoning your team can audit. Remediation agents propose a fix matched to your codebase for every confirmed vulnerability. For the technical detail on how that pipeline is built, read our whitepaper on the multi-agent approach to security code review.
Agentic review comes with trade-offs:
- More context and depth. The reviewer can find flaws that only make sense across files.
- More cost and latency. Multiple model calls and tool runs take longer than one pass over a diff.
- More need for auditable reasoning. If an agent dismisses a finding as a false positive, a person should be able to read why. Tool access also expands what the agent can touch, so teams need controls on what it's allowed to run and read.
Give Your AI Coding Assistants the Security Tools They Deserve
Where AI code review falls short
AI code review falls short in five common ways, and each one can be tuned around.
Noisy or generic comments are the most common complaint: suggestions to add a docstring, rename a variable, or "consider error handling" with no specific failure in mind. When half the comments look like that, developers stop reading all of them.
Re-reviews repeat themselves. Push a fix and some tools post the same comment again, or a slightly reworded version of it.
Results vary from run to run. LLM-only reviewers are probabilistic, so the same diff can produce different findings on different days. That makes a clean result hard to trust and hard to audit.
Large PRs hit context limits. A 3,000-line change may not fit in the model's working context, so the reviewer sees fragments and misses cross-file relationships.
AI reviews usually don't count toward approvals. On GitHub, for example, an AI reviewer's comments don't satisfy required approvals by default. That's a reasonable default, but teams sometimes assume otherwise.
An LLM-only reviewer is also not a security scanner. A model that reads a diff can spot a suspicious query, but it can't confirm whether user input reaches that query across six files, and it doesn't know whether the dependency you just bumped carries a known vulnerability. Security review needs dataflow analysis, dependency data, and secrets detection underneath the reasoning.
Teams reduce these problems in a few ways:
- Write repo instruction files that describe conventions, frameworks, and what not to comment on.
- Turn off comment types that don't earn their place, such as style suggestions your linter already enforces.
- Keep PRs small so the reviewer sees the whole change.
- Prefer tools that triage their own findings and show their reasoning.
- Keep a person on the merge decision.
Why security is where AI code review matters most
Security is where AI code review matters most because the most damaging bug classes are the ones linters, rule-based scanners, and overloaded reviewers handle worst.
Coding agents and copilots have multiplied pull request volume. Teams now ship 1,000+ PRs across hundreds of repositories, more than any security team can read with care. That code also carries real risk. In Endor Labs' Agent Security League benchmark, over 80% of the functionally correct solutions that AI coding agents produced still contained security vulnerabilities.
Broken access control, insecure design, and business logic flaws depend on what the code is supposed to do. A pattern can't describe "this user shouldn't see that invoice," and a reviewer skimming their 30th PR of the day can miss it too.
AI review that traces data across files and triages its own findings fills that gap. It reads intent like a person and checks every change like a scanner, and its reasoning stays auditable, so the security team can verify a dismissal instead of trusting it.
For how AI security review works in depth, including what it should catch and a traced example, read our guide to code security review.
How Endor Labs AI Security Review approaches it
Endor Labs AI Security Review runs the Endor Labs AI SAST engine on every pull request. One PR scan covers the changed code, any dependencies the PR changed (SCA), and any secrets it introduced. Results post as PR comments and connect to your CI/CD pipeline and policy engine on GitHub, GitLab, and Bitbucket.
The detection, triage, and remediation agents described above do the work. Each finding a developer sees has already been classified, comes with reasoning they can read, and arrives with a proposed fix. After rollout, fewer than 10% of PRs need human security review.
The evidence behind the approach:
- Across 8 real-world projects in Java, Python, JavaScript, TypeScript, C#, and Go, with ground truth set by Endor Labs' security research team, AI SAST found 192 real vulnerabilities. That's 2.6x more than Claude Code and 3.5x more than OpenAI Codex, and it surfaced 63 vulnerabilities no other tool in the test caught.
- AI SAST reduces false positives by up to 95%.
- Teams see 83% fewer blocked PRs.
PR review is one layer of a broader model. AURI by Endor Labs gives coding agents security context while they write, so fewer insecure patterns reach the PR in the first place, and AI Security Review checks every change that does.
How to roll out and measure AI code review
Roll out AI code review on a few repositories first, tune it, and measure outcomes instead of comment volume.
Pick a few repositories with active teams and a mix of languages. Run the reviewer in comment-only mode for two to four weeks and track which comments developers act on and which they dismiss. Use that data to tune instruction files and turn off low-value comment types, then expand to more repositories.
Keep security findings inside the developer's workflow. A confirmed, high-severity finding with a ready fix can fail a CI check, while a low-confidence suggestion stays a comment. Developers should see the reasoning and the fix in the same place they see the problem.
The general metrics to track:
- Review cycle time, from PR opened to merged
- Escape rate, meaning defects found after merge that the review should have caught
Security leaders need a few more:
- Confirmed true positive rate after triage. Of the findings developers see, how many turn out to be real?
- Share of PRs that still need human security review. This tells you whether the tool reduces your team's load.
- Pre-merge catch rate. Compare vulnerabilities caught before merge with those found later by scanners, pen tests, or bug bounty reports.
- Time from finding to merged fix. A finding with a working suggested fix should close in hours, not sprints.
If these numbers move in the right direction, the reviewer is earning its place. If comment volume goes up and nothing else changes, tune it or replace it.
FAQs
Does AI code review replace human code reviewers?
No, AI code review supplements human review. It handles the volume by reading every change, tracing data, and flagging likely problems. People still own architecture, threat modeling, business context, and the merge decision, and they spend less time on routine issues and more on design.
Can an AI code reviewer approve or block a pull request?
Most AI reviewers can't approve a pull request by default, but they can block one through a CI check. On GitHub, for example, an AI reviewer's review doesn't satisfy required-approval rules. Teams can configure AI review as a CI check or tie it to a policy engine, so a confirmed high-severity finding fails the check, while a person still approves the merge.
Does AI code review work with GitHub, GitLab, and Bitbucket?
Most AI code review tools support GitHub, and many also support GitLab and Bitbucket. Endor Labs AI Security Review runs on all three.
Are there open-source AI code review tools?
Yes, several open-source projects run LLM-based review on pull requests, and some can use self-hosted models so code never leaves your environment. They're a reasonable way to learn how AI review behaves on your codebase. Most rely on LLM reasoning alone, so pair them with deterministic security analysis if you plan to use them for security review.
What's next?
When you're ready to take the next step in securing your software supply chain, here are 3 ways Endor Labs can help:
