Unsupervised Approaches to Futile Cycle Detection in AI Agents

Like traditional software, AI agents are prone to failure; specifically, they can enter ‘repetitive futile cycles’ — loops of unproductive behavior that are particularly difficult to detect. This paper introduces the concept of futile cycles and distinguishes them from productive cycles in agent execution trajectories. We propose unsupervised approaches for detecting futile cycles that leverage both structural and semantic representations of agent trajectories evaluated on a large dataset of $1575$ trajectories for a LangGraph-based stock market multi-Agent application. Our hybrid approach achieves an F1 score of $0.72$ (precision: $0.62$, recall: $0.86$), significantly outperforming individual structural (F1: $0.08$) and semantic (F1: $0.28$) methods.