Detects and breaks repetitive fix loops in AI coding assistants by tracking attempts and providing escalating intervention strategies. It utilizes error fingerprinting and similarity analysis to redirect the AI toward new approaches when it gets stuck on the same error.
Provides coding agents with durable, cross-session lessons-learned memory, enforcing that success or failure verdicts can only come from human approval, human correction, or objective metrics—never from the agent itself.
Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.
