Engineer shares five lessons from reviewing 1,135 AI-generated pull requests

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A software developer spent five months running an autonomous AI coding team inside a GitHub repository, overseeing more than 1,100 agent-written pull requests. A key finding was that dry-run script output and real execution output can diverge silently, leading reviewers to draw conclusions from misleading evidence rather than actual results. The developer also found that using a different AI model for code review than the one that wrote the code helped catch errors more reliably. Agent roles defined as structured data — rather than hardcoded logic — made it easier to version, diff, and audit agent behaviour over time. The project is open-source under the AGPL-3.0 licence and is designed to run entirely on a user's own machine without a third-party service.

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