How can LLMs best allocate tasks among agents?
Self-Resource Allocation in Multi-Agent LLM Systems
April 4, 2025
https://arxiv.org/pdf/2504.02051This paper investigates how Large Language Models (LLMs) can be used to manage and optimize task allocation in multi-agent systems, similar to a project manager distributing tasks among team members. It explores different approaches, including a centralized "orchestrator" LLM that directs all agents, and a "planner" LLM that creates a plan for agents to execute independently.
Key findings for LLM-based multi-agent systems include:
- LLMs can effectively allocate tasks, with larger models performing better but at a higher cost.
- A "planner" LLM is more efficient than a centralized "orchestrator" for concurrent tasks.
- Providing explicit information about agent capabilities improves planning, particularly when dealing with less capable agents.
- The overall system performance is heavily influenced by the individual agent capabilities and how they are combined. A smaller team of more capable LLMs can outperform a larger team of mixed capabilities.