\begin{abstract}

    Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. 
    Current approaches face two critical shortcomings: they require complete architectural redesign and full retraining of all components when applied to new domains. 
    We introduce \textbf{\workforce}, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: 
    \textit{(i)} a \emph{domain-agnostic} \textbf{Planner} for task decomposition, 
    \textit{(ii)} a \textbf{Coordinator} for subtask management, and \textit{(iii)} specialized \textbf{Workers} with \emph{domain-specific} tool-calling capabilities. 
    This decoupling enables cross-domain transferability during both inference and training phases: 
    During inference, \workforce seamlessly adapts to new domains by adding or modifying worker agents; 
    For training, we introduce \owl (\shortowl), which improves generalization across domains by optimizing a domain-agnostic planner with reinforcement learning from real-world feedback.
    To validate our approach, we evaluate \workforce on the GAIA benchmark, covering various realistic, multi-domain agentic tasks.
    Experimental results demonstrate \workforce achieves open-source state-of-the-art performance (\textbf{69.70\%}), outperforming commercial systems like OpenAI's Deep Research by \textbf{2.34\%}. 
    More notably, our OWL-trained 32B model achieves \textbf{52.73\%} accuracy (\textbf{+16.37\%}) and demonstrates performance comparable to GPT-4o on challenging tasks. 
    To summarize, by enabling scalable generalization and modular domain transfer, our work establishes a foundation for the next generation of general-purpose AI assistants. 

\end{abstract}
    
    
    