OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

2025-05-29

AI Summary

To address the challenges of cross-domain transfer in multi-agent systems—namely, the need for redesign and retraining—we propose Workforce, a hierarchical multi-agent framework that decouples domain-agnostic planning (Planner-Coordinator) from domain-specific execution (modular, plug-and-play Workers), enabling zero-shot cross-domain adaptation. We introduce OWL (Online Weighted Learning), a novel reinforcement learning method that enables the planner to achieve domain-invariant generalization through optimization guided by real-world feedback. Workforce integrates tool invocation, modular Worker architecture, and hierarchical coordination mechanisms. On the GAIA benchmark, Workforce achieves state-of-the-art open-source performance (69.70%). A 32B model trained with OWL attains 52.73% accuracy—16.37 percentage points higher than the baseline—and approaches the performance of GPT-4o.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 Workforce, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: (i) a domain-agnostic Planner for task decomposition, (ii) a Coordinator for subtask management, and (iii) specialized Workers with 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 Optimized Workforce Learning (OWL), 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 (69.70%), outperforming commercial systems like OpenAI's Deep Research by 2.34%. More notably, our OWL-trained 32B model achieves 52.73% accuracy (+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.

Problem

• Enabling cross-domain transfer in multi-agent task automation
• Reducing need for full retraining when adapting to new domains
• Improving generalization via domain-agnostic planning and specialized execution

Innovation

• Hierarchical multi-agent framework for task automation
• Modular architecture with domain-agnostic Planner and Coordinator
• Optimized Workforce Learning (OWL) with reinforcement learning