From ancient water wheels to robotic process automation (RPA), automation technology has evolved throughout history to liberate human beings from arduous tasks. Yet, RPA struggles with tasks needing human-like intelligence, especially in elaborate design of workflow construction and dynamic decision-making in workflow execution. As Large Language Models (LLMs) have emerged human-like intelligence, this paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation by offloading the human labor to agents associated with construction and execution. We then instantiate ProAgent, an LLM-based agent designed to craft workflows from human instructions and make intricate decisions by coordinating specialized agents. Empirical experiments are conducted to detail its construction and execution procedure of workflow, showcasing the feasibility of APA, unveiling the possibility of a new paradigm of automation driven by agents. Our code is public at https://github.com/OpenBMB/ProAgent.

翻译：从古代水车到机器人流程自动化（RPA），自动化技术贯穿历史不断演进，旨在将人类从繁重任务中解放出来。然而，RPA在需要类人智能的任务中面临挑战，尤其是在工作流的精细化构建设计和执行过程中的动态决策方面。随着大语言模型（LLM）展现出类人智能，本文提出智能体流程自动化（APA）——一种突破性的自动化范式，通过将构建与执行环节的人类劳动转移至基于LLM的智能体，利用该类智能体实现高级自动化。我们进而实例化了ProAgent，这是一种基于LLM的智能体，能够根据人类指令构建工作流，并通过协调专用智能体做出复杂决策。通过实证实验详细阐述了其工作流的构建与执行流程，验证了APA的可行性，揭示了由智能体驱动的新型自动化范式的可能性。我们的代码已开源在https://github.com/OpenBMB/ProAgent。