How can LLMs manage complex tasks with multiple agents?
Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
This paper introduces Magenic-One, a multi-agent system for solving complex tasks using a team of specialized AI agents (Coder, Computer Terminal, FileSurfer, WebSurfer) coordinated by a lead agent (Orchestrator). The system aims to be a generalist, capable of handling diverse tasks like web browsing, file manipulation, and code execution.
Key points for LLM-based multi-agent systems: Magenic-One employs LLMs for most agents, demonstrating the viability of a multi-agent approach for complex problem-solving. The modular design allows easy extension and adaptation by swapping or adding agents. The Orchestrator's role highlights the importance of planning, task delegation, progress tracking, and error recovery in multi-agent systems. The paper also discusses the challenges of evaluating and debugging multi-agent systems, as well as the associated risks (e.g., security, misinformation) and potential mitigations. Finally, it introduces AutoGenBench, a tool for repeatable and controlled evaluation of agentic systems.