W ork T eam: Constructing Workflows from Natural Language with Multi-Agents (2025)

Abstract: 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.

## introduction workflows, comprising reusable processes that integrate multiple tools or components in a specific logic sequence, can significantly enhance enterprise efficiency @xcite . traditional workflow construction methods require numerous manual steps to orchestrate components, demanding specialized expertise @xcite @xcite . in contrast, automated commercial systems can directly convert natural language instructions into workflows, offering a more convenient and technically accessible approach. with the rapid development of large language models (llms) @xcite every weekday morning at 9:00 am, send me a text message reminder to clock in for work. the phone number is 12714532889. crafting a workflow (figure 1 ) for real-life scenarios involves coordinating several tasks, from comprehending human intent, selecting appropriate components, to orchestrating the task flow and accurately configuring each component's parameters @xcite ). it's quite challenging to rely on a single llm agent to handle the entire process, as different tasks may require specialized knowledge and skills. the need to switch between multiple tasks could potentially affect its performance on any individual task @xcite . to address this challenge, we draw inspiration from software development, where requires collaboration among multiple team members with diverse skill sets is essential @xcite . specifically, we propose work-team, a multi-agent framework that integrates multiple agents to collaboratively accomplish the nl2workflow task. workteam consists of three agents with distinct roles: the supervisor, the orchestrator and the filler (figure 2 ). the supervisor agent is responsible for understanding the user's intent and coordinating the orchestrator agent and the filler agent. upon receiving the user intent parsed by the supervisor agent, the orchestrator agent selects the appropriate components and arranges them into a suitable workflow schema. the filler agent then retrieves the documentation for relevant components and fills in accurate parameters, turning it into a fully operational workflow. our framework enables different agents to perform their respective tasks accurately and communicate efficiently, thereby effectively constructing workflows. moreover, since no publicly available nl2workflow benchmarks exist, we construct the hw-nl2workflow dataset from real production scenarios, comprising 3,695 entries for training and evaluation. extensive experiments show that workteam significantly improves workflow construction accuracy compared to existing methods, and further analysis validates the effectiveness of our framework. our contributions are summarized as follows: ## methods the workteam framework comprises three agents: the supervisor agent, the orchestrator agent, and the filler agent. the overall structure of the framework is shown in figure 2 . upon receiving an end user's prompt, the supervisor agent initiates a task planning phase, decomposing the tasks into subtasks and invoking the orchestrator and filler agents in a coordinated manner to execute them. the orchestrator and filler agents handle component orchestration and parameter filling, respectively, using appropriate tools to complete these tasks. to further elucidate the functionality of workteam, figure 6 in appendix b provides an operational example. the design and functionality of these agents are detailed as following. ## hw-nl2workflow given the limited availability of publicly accessible datasets for nl2workflow tasks and our focus on real-world commercial applications, we have developed hw-nl2workflow, a novel dataset specifically designed to meet these needs. this dataset consists of 3,695 real-world enterprise workflows, making it suitable for both performance evaluation and tool training. ## conclusion in this paper, we present workteam, a novel multiagent framework designated to enhance workflow automation in enterprise environments. three specialized agents -supervisor, orchestrator, and filler agents -collaborate to overcome the limitations of a traditional llm agent-based method, resulting in substantial improvements to workflow generation accuracy. experimental results on the hw-nl2workflow dataset confirm the effectiveness of workteam. to address the lack of publicly available nl2workflow benchmarks, we develop the hw-nl2workflow dataset, comprising 3,695 real-world business samples, to support research in this area. future work will focus on refining the framework to support more complex workflows and integrate it with a wider range of enterprise tools to further enhance automation.