Initially, the AI code reviewer produced too many low-value comments and false positives, causing developers to lose trust.
They added explicit reasoning logs so the AI must explain its findings before flagging issues, enabling better debugging and fewer arbitrary alerts.
They reduced the toolset to only essential components, which improved the reviewer’s precision by removing confusion from unused tools.
They replaced a single monolithic agent with specialized micro-agents focused on specific tasks, further increasing accuracy.
These changes led to a 51% reduction in false positives and halved the number of comments per pull request.
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