Finding Root Causes, Not Symptoms: A Mechanism and Method for Agent Failure Attribution

When a multi-agent LLM system fails over a long run, naming the responsible agent is often the easy part. Pinpointing the step where the failure began is hard, and hardest on the long trajectories that matter most. We find that on such runs the dominant error is an omission: a necessary action is silently skipped, so it leaves no local trace and surfaces only later as a downstream symptom. Attributors that score steps by local evidence are therefore pulled toward that late symptom, a systematic bias we call symptom attraction. But what the run needed to do is determined by the task, not by the trajectory. From the question and its correct answer, which we call the goal, the conditions that any successful run must meet can be listed. The condition the skipped action would have met is on that list, even though no step in the trajectory shows it. GCCA (Goal-Conditioned Causal Attribution) turns the goal into a chain of necessary preconditions and blames the first step that leaves one unmet. On Who&When, GCCA is the best zero-shot attributor at both naming the agent (70.1%) and pinpointing the step (23.0%) on the long, omission-heavy subset. It also cuts the late bias from as much as +15.7 steps to a near-zero +1.03. The bias that reveals the problem is thus the bias GCCA removes. GCCA stays competitive on short trajectories, uses the fewest tokens at 4.7k per sample against 8–67k, and holds up across five backbones.