OrgAgent: Organize Your Multi-Agent System like a Company
This addresses the challenge of effective coordination in multi-agent reasoning for AI researchers and practitioners, though it is incremental as it builds on existing hierarchical concepts.
The paper tackles the problem of organizing multi-agent systems by introducing OrgAgent, a company-style hierarchical framework that improves performance and reduces token consumption, achieving a 102.73% performance gain and 74.52% token reduction on SQuAD 2.0 with GPT-OSS-120B.
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. For example, for GPT-OSS-120B, the hierarchical setting improves performance over flat multi-agent system by 102.73% while reducing token usage by 74.52% on SQuAD 2.0. Further analysis shows that hierarchy helps most when tasks benefit from stable skill assignment, controlled information flow, and layered verification. Overall, our findings highlight organizational structure as an important factor in multi-agent reasoning, shaping not only effectiveness and cost, but also coordination behavior.