Mining Failure Modes from Agent Trajectories at Scale
Agent developers inspect failed runs by hand, which does not scale beyond a few dozen trajectories. We propose a clustering approach that groups failures by the step at which the trajectory diverges from a successful reference run, and label the clusters with short natural-language descriptions. On 18,000 trajectories from three agent frameworks, the method recovers 83% of failure categories identified by manual analysis and surfaces four categories that were previously unreported.