CAUM – 80K AI agent sessions analyzed. 88.7% loops fail. AUC=0.814
SMRTR summary
CAUM analyzes AI agent behavior patterns without reading prompts, detecting when agents get stuck in unproductive loops that lead to failure 88.7% of the time. After studying over 80,000 real agent sessions, the system achieved 81.4% accuracy in predicting session outcomes and could potentially save $1.7 million annually in wasted compute costs.
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