How to automate LLM multi-agent failure attribution?
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
This paper introduces the problem of automated failure attribution in Large Language Model (LLM) multi-agent systems: automatically identifying which agent is responsible for a task failure and when the decisive error occurred. A new dataset, "Who&When," containing annotated failure logs from 127 LLM multi-agent systems, is created to facilitate this research. Three LLM-based failure attribution methods (all-at-once, step-by-step, and binary search) are evaluated on Who&When, revealing that providing the LLM with the complete conversation improves agent identification, while incremental processing is better for pinpointing error steps. While even advanced reasoning LLMs struggle with the task, combining methods and statistical analysis shows promise. This research highlights the complexity of failure analysis in LLM multi-agent systems and the need for further research in automated failure attribution.