Why One Review Pass Is Not Enough for AI-Generated Code
AI now writes a large and growing share of production code. That shift has quietly changed what code review has to catch, and the single-pass tools most teams still rely on were never designed for it. This is a look at why multi-pass, cross-model review is becoming standard infrastructure, what it catches that a linter cannot, and where it still needs a human in the loop.
How AI-Written Code Changed the Review Problem
When a human writes a function, the bugs tend to cluster around the parts they found hard. AI-generated code is different. It is fluent and confident everywhere, which means errors hide inside code that reads perfectly. A model can produce a well-structured function with a subtle off-by-one, a missing null check on a new path, or an assumption that only breaks when two features interact.
Traditional linters and static analyzers match known patterns, so they sail right past logic errors that are locally valid but globally wrong. As AI-authored commits climb past 40 percent in many codebases, the volume of this specific failure mode has outgrown what a single automated pass can handle.
What Multi-Pass Review Actually Does
Multi-pass review runs the same change through several review stages instead of one, and often through more than one model. Each pass has a job. A security pass looks for injection points, unsafe deserialization, and leaked secrets. A logic pass traces control flow across files to find broken assumptions. A performance pass flags accidental quadratic loops or redundant queries.
Because each pass is focused, it surfaces issues the others miss, and running different models reduces the blind spots any single model shares with the one that wrote the code. The result behaves less like a linter and more like a panel of specialist reviewers. We broke the architecture down in detail here.
What It Catches, and What It Still Misses
In practice, multi-pass review is strong at the things that hurt most: cross-file logic errors, security regressions, and edge cases the tests do not cover. It is also good at explaining its findings, which shortens the fix loop.
What it still misses is intent. A model can confirm that code is correct without knowing it solves the wrong problem, and it will produce false positives that need human triage. The honest framing is that AI review is an extremely fast first reviewer, not a final authority.
The Takeaway
The teams getting the most from AI-generated code are not the ones reviewing it the least. They are the ones matching a faster writer with a faster, deeper reviewer, then pointing scarce human attention at architecture and intent. Multi-pass, cross-model review is how that balance holds as the share of AI-authored code keeps climbing.