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arXiv · 2408.14972

AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems

Chi-Min Chan·Jianxuan Yu·Weize Chen·Chunyang Jiang·Xinyu Liu·Weijie Shi·Zhiyuan Liu·Wei Xue·Yike Guo
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Abstract

The rapid advancement of large language models (LLMs) has led to the rise of LLM-based agents. Recent research shows that multi-agent systems (MAS), where each agent plays a specific role, can outperform individual LLMs. However, configuring an MAS for a task remains challenging, with performance only observable post-execution. Inspired by scaling laws in LLM development, we investigate whether MAS performance can be predicted beforehand. We introduce AgentMonitor, a framework that integrates at the agent level to capture inputs and outputs, transforming them into statistics for training a regression model to predict task performance. Additionally, it can further apply real-time corrections to address security risks posed by malicious agents, mitigating negative impacts and enhancing MAS security. Experiments demonstrate that an XGBoost model achieves a Spearman correlation of 0.89 in-domain and 0.58 in more challenging scenarios. Furthermore, using AgentMonitor reduces harmful content by 6.2% and increases helpful content by 1.8% on average, enhancing safety and reliability. Code is available at \url{https://github.com/chanchimin/AgentMonitor}.

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Chi-Min Chan, Jianxuan Yu, Weize Chen, Chunyang Jiang, Xinyu Liu, Weijie Shi, Zhiyuan Liu, Wei Xue, Yike Guo. 2024-08-27. AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems. https://arxiv.org/abs/2408.14972

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