I Tested AI Code Review Against My Own Agentic Workflow. Here’s What It Actually Catches.

“AI code review tools” pulls around 720 searches a month, mostly from engineering leaders trying to figure out whether these tools are worth adopting or just another subscription that sits unused. As a Principal Engineer who runs most of my work through agentic AI workflows daily, here’s an honest answer: it depends entirely on what you’re asking it to catch. What AI Code Review Is Actually Good At The strongest use case, by far, is the boring stuff a human reviewer’s attention tends to skip past: inconsistent naming, a missed null check, an unused import, a pattern that doesn’t match the rest of the codebase. AI review tools are relentlessly consistent about the small, mechanical things. They don’t get tired on the fortieth file of a large pull request the way a human reviewer does. They’re also genuinely useful as a first pass before a human ever looks at the code, catching the obvious stuff so the human reviewer’s limited attention goes to the parts that actually need judgment.…