Fewer, sharper AI code review comments build more developer trust than volume

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A software developer building an open-source CLI tool called klaussy-agents found that AI-powered code review agents tend to flood pull requests with unnecessary comments, burying the two or three genuinely critical findings among dozens of irrelevant ones. When models are prompted to treat an empty review as a valid and successful outcome, rather than a sign of failure, they stop padding results with low-value observations. The author encoded specific rules into the agent, including requiring each finding to cite a concrete trigger — an actual input or execution path that causes the problem — which filters out vague style opinions and unverifiable claims. Self-assigned confidence scores were also banned, as they allow weak findings to ship with a hedge rather than being discarded. The core design principle is precision over recall: a review agent that reliably surfaces two real issues earns developer trust, while one that generates twenty mixed-quality comments trains authors to stop reading altogether.

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