How to plan tasks for LLM agents?
AGENT-ORIENTED PLANNING IN MULTI-AGENT SYSTEMS
October 4, 2024
https://arxiv.org/pdf/2410.02189This research paper proposes a framework for agent-oriented planning in multi-agent AI systems. The framework breaks down user queries into smaller sub-tasks and assigns them to specialized AI agents (like code or search agents) for execution.
Key points for LLM-based multi-agent systems:
- Efficient task allocation: A reward model predicts which agents are best suited for each sub-task, minimizing unnecessary agent calls.
- Task modification mechanisms: The system refines sub-tasks based on their complexity and the agent's capabilities, including replanning, detailing, or re-describing them.
- Completeness and non-redundancy: A dedicated component analyzes sub-tasks to ensure they collectively cover the user query without redundant information.
- Continuous learning: The system incorporates feedback from completed tasks to improve future task decomposition and agent selection.