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.

翻译：工作流通过编排涉及多种工具或组件的复杂流程，在提升企业效率方面发挥着至关重要的作用。然而，手工构建工作流需要专业知识，存在较高的技术壁垒。近期，大语言模型的发展提升了从自然语言指令生成工作流（亦称NL2Workflow）的能力，但现有的基于单一LLM智能体的方法在处理复杂任务时，由于需要专业知识以及任务切换带来的负担，面临性能下降的问题。为应对这些挑战，我们提出了WorkTeam，一个多智能体NL2Workflow框架，包含主管、编排器和填充器三个具有不同角色的智能体，它们协同工作以增强转换过程。由于目前尚无公开的NL2Workflow基准数据集，我们还引入了HW-NL2Workflow数据集，其中包含3,695个用于训练和评估的真实业务样本。实验结果表明，我们的方法显著提高了工作流构建的成功率，为企业NL2Workflow服务提供了一种新颖且有效的解决方案。