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AI code review works best as a first pass: it reads every diff with full attention, flags bugs, risky patterns, and missing tests, and lets human reviewers spend their time on design and intent instead of typo-hunting. The goal is a pipeline where nothing reaches a human reviewer without a machine pass first.
Decide where the review runs: locally before commit (fastest feedback), on pull request open (most common), or both. PR-time review is easiest to adopt because it changes nobody's local habits.
A diff alone produces shallow comments. Feed the model the surrounding files, the PR description, and your conventions (lint rules, error-handling patterns) so its findings match how your codebase actually works.
Ask for concrete claims — 'this branch drops the error on line 42' — not style opinions. Require file/line references so every finding can be checked in seconds, and have it rank findings by severity.
The first week will surface false positives. Maintain a short suppression list (patterns the tool gets wrong in your codebase) and re-check it monthly — an AI reviewer people ignore is worse than none.
Machine review frees humans for the questions machines answer badly: is this the right abstraction, does this match where the product is going, should this exist at all. Make that split explicit in your review guidelines.
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Your agent installs the app, walks every screen, reads the actual pixels, finds what code review can't — then fixes it.
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