多智能体协作让自然语言自动生成企业工作流，效率更高。
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents
- 用三个分工不同的智能体协同生成工作流
- 在3695个真实业务样本上成功率显著提升
- 适合需要自动化流程设计的企业用户
工作流在协调多工具复杂流程中对提升企业效率至关重要。然而，手工构建工作流需专业知识，存在技术门槛。近年来大语言模型（LLMs）在自然语言转工作流（NL2Workflow）任务上取得进展，但现有单智能体方法在复杂任务上因需专业领域知识和频繁任务切换而性能下降。为此，我们提出WorkTeam，一个包含监督者、协调者和填充者的多智能体框架，各角色分工协作以增强转换效果。由于目前尚无公开的NL2Workflow基准，我们还构建了HW-NL2Workflow数据集，包含3,695个真实企业业务样本用于训练与评估。实验表明，该方法显著提高了工作流构建的成功率，为企业的NL2Workflow服务提供了新范式。
原文摘要 · Abstract (English)
Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction requires expert knowledge, presenting significant technical barriers. Recent advancements in Large Language Models (LLMs) have improved the generation of workflows from natural language instructions (aka NL2Workflow), yet existing single LLM agent-based methods face performance degradation on complex tasks due to the need for specialized knowledge and the strain of task-switching. To tackle these challenges, we propose WorkTeam, a multi-agent NL2Workflow framework comprising a supervisor, orchestrator, and filler agent, each with distinct roles that collaboratively enhance the conversion process. As there are currently no publicly available NL2Workflow benchmarks, we also introduce the HW-NL2Workflow dataset, which includes 3,695 real-world business samples for training and evaluation. Experimental results show that our approach significantly increases the success rate of workflow construction, providing a novel and effective solution for enterprise NL2Workflow services.
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