ICLR 2025

Published as a conference paper at ICLR 2025
Agent-Oriented Planning in Multi-Agent Systems
Ao Li1,2,∗Yuexiang Xie3 Songze Li4,5 Fugee Tsung1,2 Bolin Ding3 Yaliang Li3,†
1The Hong Kong University of Science and Technology (Guangzhou)
2The Hong Kong University of Science and Technology 3Alibaba Group 4Southeast University
5Engineering Research Center of Blockchain Application, Supervision and Management
(Southeast University), Ministry of Education
aliat@connect.ust.hk, songzeli@seu.edu.cn, season@ust.hk,
{yuexiang.xyx,bolin.ding,yaliang.li}@alibaba-inc.com
ABSTRACT
Through the collaboration of multiple LLM-empowered agents possessing diverse
expertise and tools, multi-agent systems achieve impressive progress in solving
real-world problems. Given the user queries, the meta-agents, serving as the brain
within multi-agent systems, are required to decompose the queries into multi-
ple sub-tasks that can be allocated to suitable agents capable of solving them,
so-called agent-oriented planning. In this study, we identify three critical de-
sign principles of agent-oriented planning, including solvability, completeness,
and non-redundancy, to ensure that each sub-task can be effectively resolved, re-
sulting in satisfactory responses to user queries. These principles further inspire
us to propose AOP, a novel framework for agent-oriented planning in multi-agent
systems, leveraging a fast task decomposition and allocation process followed by
an effective and efficient evaluation via a reward model. According to the eval-
uation results, the meta-agent is also responsible for promptly making necessary
adjustments to sub-tasks and scheduling. Besides, we integrate a feedback loop
into AOP to further enhance the effectiveness and robustness of such a problem-
solving process. Extensive experiments demonstrate the advancement of AOP
in solving real-world problems compared to both single-agent systems and exist-
ing planning strategies for multi-agent systems. The source code ...