Signal TRAJDEBUG Traces Error Lifecycle to Pinpoint Root Causes in Long-Horizon AI Agent Failures

Summary

A preprint by Yunjia Qi, Zehua Yin, Xintong Shi, Hao Peng and colleagues notes that LLM-based agent systems show impressive capability in complex domains while suffering from cascading errors that are hard to debug. Critical error detection aims to find the earliest error step in a failed trajectory that caused the eventual failure, but faces two obstacles: long trajectories make individual errors hard to isolate, and errors can propagate and compound before they surface as observable failures. The authors introduce TRAJDEBUG, a method that traces the lifecycle of errors across long-horizon agent trajectories to pinpoint critical failure points more precisely. The method aims to make debugging complex, multi-step AI agent behavior more manageable. The authors present it as a tool for improving the reliability of agent systems going forward.