The pull request review is where a lot of AI code tools stop being useful. You ask one model to read a 40-file diff, it returns six surface comments — formatting nits, an obvious null check, a TODO it spotted — and misses the race condition that ships to production on Friday. AdamsReview, an open-source project from Adam J. G. Miller, takes a different swing at the problem: instead of one model passing once over the diff, it orchestrates several Claude Code agents that each look at the change through a different lens, then consolidates their output into a single review.
Why single-pass LLM reviews leave bugs on the table
Single-agent review has three failure modes you can reproduce on almost any non-trivial PR.
First, attention dilution. When a 2,000-line diff lands in one prompt, the model spreads its attention thin. The first few files get genuine engagement; by the time the model is reading the last test file, it is mostly pattern-matching. Multi-agent setups sidestep this by giving each agent a smaller surface area or a narrower question to answer.
Second, no adversarial perspective. A single pass tends to validate the change rather than attack it. The model reads the diff as written and asks "is this consistent with itself?" rather than "what is the worst input I can think of that breaks this?" You can prompt your way around it, but the moment you ask one agent to do both — write a constructive review and try to break the code — the adversarial half loses.
