All It Takes Is One Prompt: An Autonomous LLM-MA System
Abstract: LLM-powered multi-agent (LLM-MA) systems have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent, a framework designed for autonomous coordination in LLM-MA systems. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication, and comprehensive system monitoring of agents. In evaluations, MegaAgent demonstrates exceptional performance, successfully developing a Gobang game within 800 seconds and scaling up to 590 agents in a national policy simulation to generate multi-domain policies. It significantly outperforms existing systems, such as MetaGPT, in both task completion efficiency and scalability. By eliminating the need for predefined SOPs, MegaAgent demonstrates exceptional scalability and autonomy, setting a foundation for advancing true autonomy in LLM-MA systems.\footnote{Code is available at \url{https://anonymous.4open.science/r/MegaAgent-dev-DEF0}}
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: NLP Applications,Generation
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 858
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