OrgAgent: Organize Your Multi-Agent System like a Company
Yiru Wang, Xinyue Shen, Yaohui Han, Michael Backes, Pin-Yu Chen, Tsung-Yi Ho

TL;DR
OrgAgent introduces a hierarchical, company-style framework for multi-agent systems that improves reasoning performance and reduces token usage by structuring agents into governance, execution, and compliance layers.
Contribution
The paper proposes a novel hierarchical organization for multi-agent systems inspired by companies, demonstrating its advantages over flat structures in reasoning tasks.
Findings
Hierarchical multi-agent systems outperform flat ones in reasoning accuracy.
Hierarchy reduces token consumption significantly in large language model-based systems.
Stable skill assignment and layered verification benefit most from the hierarchical structure.
Abstract
While large language model-based multi-agent systems have shown strong potential for complex reasoning, how to effectively organize multiple agents remains an open question. In this paper, we introduce OrgAgent, a company-style hierarchical multi-agent framework that separates collaboration into governance, execution, and compliance layers. OrgAgent decomposes multi-agent reasoning into three layers: a governance layer for planning and resource allocation, an execution layer for task solving and review, and a compliance layer for final answer control. By evaluating the framework across reasoning tasks, LLMs, execution modes, and execution policies, we find that multi-agent systems organized in a company-style hierarchy generally outperform other organizational structures. Besides, hierarchical coordination also reduces token consumption relative to flat collaboration in most settings.…
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