CauterRule v0.2.0: Specific AI Agent Rules Still Fire on Successful Runs

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CauterRule, an open-source tool that converts repeated AI agent failures into reusable standing rules, released version 0.2.0 after testing four models across 745 trajectories. The system successfully kept generic trigger rates below 5.6%, well under its 10% target, with most triggers precisely naming tools and error conditions such as 'git push fails with non-fast-forward'. However, 6 of 10 golden test scenarios remained inconclusive because these specific triggers also matched clean, successful runs — meaning they could disrupt working workflows if promoted. The replay engine flagged all six as ambiguous evidence, unable to safely approve or reject them. The findings highlight a core challenge: a trigger can accurately describe a failure condition while still being too broad in terms of when it actually fires.

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