Despite growing enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains on popular benchmarks often remain minimal compared with single-agent frameworks. This gap highlights the need to systematically analyze the challenges hindering MAS effectiveness.We present MAST (Multi-Agent System Failure Taxonomy), the first empirically grounded taxonomy designed to understand MAS failures. We analyze seven popular MAS frameworks across over 200 tasks, involving six expert human annotators. Through this process, we identify 14 unique failure modes, organized into 3 overarching categories, (i) specification issues, (ii) inter-agent misalignment, and (iii) task verification. MAST emerges iteratively from rigorous inter-annotator agreement studies, achieving a Cohen's Kappa score of 0.88. To support scalable evaluation, we develop a validated LLM-as-a-Judge pipeline integrated with MAST. We leverage two case studies to demonstrate MAST's practical utility in analyzing failures and guiding MAS development. Our findings reveal that identified failures require more complex solutions, highlighting a clear roadmap for future research. We open source our comprehensive dataset and LLM annotator to facilitate further development of MAS.
View on arXiv@article{cemri2025_2503.13657,
title={ Why Do Multi-Agent LLM Systems Fail? },
author={ Mert Cemri and Melissa Z. Pan and Shuyi Yang and Lakshya A. Agrawal and Bhavya Chopra and Rishabh Tiwari and Kurt Keutzer and Aditya Parameswaran and Dan Klein and Kannan Ramchandran and Matei Zaharia and Joseph E. Gonzalez and Ion Stoica },
journal={arXiv preprint arXiv:2503.13657},
year={ 2025 }
}