MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines

Yaolun Zhang, Xiaogeng Liu, Chaowei Xiao

TL;DR: We introduce a new framework that can automatically produce multi-agent systems base on finite state machines

Abstract: Large Language Models (LLMs) have demonstrated the ability to solve a wide range of practical tasks within multi-agent systems. However, existing human-designed multi-agent frameworks are typically limited to a small set of pre-defined scenarios, while current automated design methods suffer from several limitations, such as the lack of tool integration, dependence on external training data, and rigid communication structures. In this paper, we propose \textbf{MetaAgent}, a \textbf{finite state machine} based framework that can automatically generate a multi-agent system. Given a task description, MetaAgent will design a multi-agent system and polish it through an optimization algorithm. When the multi-agent system is deployed, the finite state machine will control the agent's actions and the state transitions. To evaluate our framework, we conduct experiments on both text-based tasks and practical tasks. The results indicate that the generated multi-agent system surpasses other auto-designed methods and can achieve a comparable performance with the human-designed multi-agent system, which is optimized for those specific tasks.

Lay Summary:

Decomposing a complex task, such as software development, and assigning its parts to multiple collaborating AI agents is a paradigm known as a multi-agent system. However, most existing multi-agent systems rely on manual, human design, resulting in fixed collaboration workflows that are limited to predefined scenarios. When an error from a previous step is discovered, these rigid structures often lack the ability to backtrack for corrections.

Our research, MetaAgent, is a framework designed to address this challenge by automatically constructing a multi-agent system from a given task description. The core of MetaAgent is its use of a mathematical model called a "Finite State Machine" (FSM) to organize the agents. This can be understood as a dynamic blueprint that not only defines which agent is responsible for each stage but also specifies the precise conditions required to transition to the next state.

This FSM-based structure provides two key advantages: Firstly, it supports agents that can use external tools, such as a search engine or code interpreter, to interact with the world and solve complex problems. It also enables "State Traceback," a capability allowing the system to return to a previous state to fix issues. For instance, if a tester agent finds a bug in the software, the system can transition back to the programmer agent's state for refinement.

Experiments show that systems generated by MetaAgent outperform other automated design methods and achieve performance comparable to—and sometimes exceeding—human-designed systems that were specifically optimized for those tasks. MetaAgent provides an efficient, automated paradigm for creating more powerful and adaptive multi-agent systems, lowering the development barrier and enhancing their ability to solve complex, real-world problems

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Primary Area: Deep Learning->Large Language Models

Keywords: LLM Agent, Multi-Agent System

Submission Number: 13114