ProAgent：从机器人流程自动化到主体流程自动化

ProAgent: From Robotic Process Automation to Agentic Process Automation

November 2, 2023

作者: Yining Ye, Xin Cong, Shizuo Tian, Jiannan Cao, Hao Wang, Yujia Qin, Yaxi Lu, Heyang Yu, Huadong Wang, Yankai Lin, Zhiyuan Liu, Maosong Sun

cs.AI

摘要

从古代水车到机器人流程自动化（RPA），自动化技术在历史上不断发展，旨在解放人类免于繁重任务。然而，RPA 在需要类人智能的任务中面临困难，特别是在复杂的工作流设计和工作流执行中的动态决策方面。随着大型语言模型（LLMs）具备了类人智能，本文介绍了一种名为主体过程自动化（APA）的开创性自动化范式，利用基于LLM的代理实现先进自动化，通过将人类劳动转移给与构建和执行相关的代理。然后，我们实例化了ProAgent，一种基于LLM的代理，旨在根据人类指令制定工作流程，并通过协调专门代理进行复杂决策。进行了实证实验，详细说明了其工作流程的构建和执行过程，展示了APA的可行性，揭示了由代理驱动的自动化新范式的可能性。我们的代码公开在https://github.com/OpenBMB/ProAgent。

English

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.