  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.
