OrgAgent: Organize Your Multi-Agent System like a Company
Summary: arXiv:2604.01020v1 Announce Type: cross
As artificial intelligence continues to advance, the development of large language model-based multi-agent systems is gaining traction. These systems have demonstrated significant capabilities in complex reasoning tasks. However, the organization of multiple agents within these systems remains a challenging question. In response, we introduce OrgAgent, a hierarchical multi-agent framework designed to emulate a company structure, separating collaboration into governance, execution, and compliance layers.
Framework Overview
OrgAgent decomposes multi-agent reasoning into three distinct layers:
- Governance Layer: Responsible for planning and resource allocation.
- Execution Layer: Focused on task solving and review processes.
- Compliance Layer: Ensures final answer control and verification.
Performance Evaluation
To assess the effectiveness of the OrgAgent framework, we conducted a series of evaluations across various reasoning tasks, utilizing large language models (LLMs), execution modes, and execution policies. The results indicate that multi-agent systems organized in a company-style hierarchy generally outperform other organizational structures. Key findings include:
- Improvement in performance: For instance, when utilizing GPT-OSS-120B, the hierarchical setting achieved a performance increase of 102.73% over flat multi-agent systems on the SQuAD 2.0 benchmark.
- Reduction in token consumption: The hierarchical approach resulted in a 74.52% decrease in token usage compared to flat collaboration in most settings.
Benefits of Hierarchical Coordination
Our analysis reveals that the hierarchical coordination model is particularly beneficial in scenarios where:
- Tasks require stable skill assignments, ensuring that agents are consistently matched to tasks suited to their abilities.
- Controlled information flow is necessary, allowing for regulated communication between the different layers of the system.
- Layered verification can enhance the accuracy and reliability of the final outputs, as each layer contributes to the overall assessment of the task.
Conclusion
The findings from our research underscore the importance of organizational structure in multi-agent reasoning systems. By adopting a hierarchical framework like OrgAgent, developers can significantly improve not only effectiveness and cost efficiency but also coordination behavior among agents. As multi-agent systems continue to evolve, the insights gained from this study will be pivotal in shaping future research and applications in the field of artificial intelligence.