AI code review has exploded in the last year. Everyone has a different setup — I'm curious what's actually working.
A few specific questions:
1. Do you use a single reviewer or multiple specialized ones?
Claude's built-in pr-review-toolkit runs 6 parallel sub-agents (tests, types, silent failures, etc.). I've been experimenting with the opposite — one agent, one deeply focused skill per review. Different trade-offs.
2. Do you apply the same review to every file, or do you route by context?
A Clean Code reviewer on a domain model file gives you naming feedback when the real problem is your aggregate boundary. A DDD reviewer on a utility function talks about bounded contexts when you just need cleaner variable names. How do you handle this?
3. What does your AI reviewer consistently miss?
In my own benchmark, the biggest miss was a PCI violation — card data logged to stdout. The architectural reviewer caught naming issues and design patterns but had no security lens at all.
4. Pre-merge gate or architectural review — or both?
I've landed on "both, at different moments" — fast confidence-filtered review before merge, deeper book-grounded review when planning a larger refactor.
I wrote up a benchmark comparing Claude's native reviewer against a routed book-based approach — full comparison here if you want the details. Curious whether others have run similar experiments or landed on different conclusions.
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