AI Review Loops Don't Always Stabilise

# AI Review Loops Don't Always Stabilise Source: [https://kevinmahoney.co.uk/articles/ai-review-loops/](https://kevinmahoney.co.uk/articles/ai-review-loops/) 25 August 2026 It’s tempting to think that having AI review code and then implement fixes in a loop will create a flawless diamond at the end of the process\. Some are accidentally doing the slow version of this loop where dev A creates an AI\-generated PR, dev B lazily AI\-reviews it, then dev A AI\-fixes it,*ad infinitum*\. With careful guardrails this can work, but doing it naively will often create a mess for a few reasons: - AI does not have a consistent set of opinions\. What it considers good code can change from run to run\. In the worst case, it can flip\-flop from review to review, causing a never\-ending loop\. - Reviews can often encourage scope creep, from ‘Hey, you don’t have a test for this\!’, to ‘Hey, you don’t have a CI/CD pipeline\!’, to ‘Hey, you don’t have an Android app for this\!’\. - Any false positives or hallucinations in reviews can introduce new defects\. As a quick test I asked Opus 5 to generate a small amount of ‘perfect, non\-trivial’ code and passed it through three review\-fix loops\. Here is an[AI\-generated writeup](https://gist.github.com/KMahoney/3098f0f12638d0a83a5ef3b91bef601d)\. Note the defect count*increases*with each review\!