\section{Conclusion}

We introduced \workforce, a hierarchical multi-agent framework that decouples strategic planning from domain-specific execution, enabling cross-domain transferability without system redesign. \workforce achieves \textbf{69.70\%} accuracy on the GAIA benchmark, outperforming both open-source alternatives and Deep Research.
We also presented \owl (\shortowl), which applies reinforcement learning and enhances the Qwen2.5-32B-Instruct model by \textbf{16.37\%} on the GAIA benchmark.
We hope that by combining plug-and-play worker nodes with a generalizable planning core, our approach provides a scalable foundation for general-purpose AI assistants. 
% The principles of modularity and targeted optimization in \workforce and \owl advance the field toward more flexible and powerful multi-agent systems.

% The modular design of \workforce creates opportunities for future multi-agent systems research. 

\section{Acknowledgements}

We sincerely thank Xukun Liu and Weiyu Ma, Weijie Bai, Yuqin Xie, and Yueming Lai for their valuable support in this project. This work was carried out as a collaborative open source research initiative at CAMEL-AI.org, supported by funding from Eigent.AI.

% We want to thank the engineering team, including Yifeng, Tao, and Qianshuo, led by Wendong, for their valuable support in maintaining the open-source implementation of our project. Their efforts in improving code stability, adding auxiliary features, and managing community feedback have helped enhance the usability and visibility of the project. While the research, design, and writing of this paper were conducted independently, the continued maintenance work by the team played a supportive role in sustaining the project's public engagement.