# ProAgent: From Robotic Process Automation to Agentic Process Automation

arXiv: https://arxiv.org/abs/2311.10751 · HTML: https://mlsanity.com/paper/2311.10751/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 35 of its 50 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 35)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [Large Language Models as Tool Makers](https://mlsanity.com/paper/2305.17126/) (2023)
- [Tool Learning with Foundation Models](https://mlsanity.com/paper/2304.08354/) (2023)
- [WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://mlsanity.com/paper/2305.06849/) (2023)
- [Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models](https://mlsanity.com/paper/2308.10379/) (2023)
- [Rational Decision-Making Agent with Internalized Utility Judgment](https://mlsanity.com/paper/2308.12519/) (2023)
- [Robotic Process Automation -- A Systematic Literature Review and Assessment Framework](https://mlsanity.com/paper/2012.11951/) (2020)
- [From Robotic Process Automation to Intelligent Process Automation: Emerging Trends](https://mlsanity.com/paper/2007.13257/) (2020)
- [On the Evaluation of Intelligent Process Automation](https://mlsanity.com/paper/2001.02639/) (2020)
- [Towards Intelligent Robotic Process Automation for BPMers](https://mlsanity.com/paper/2001.00804/) (2020)

## Built on by (most cited first, 26 of 26)

- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [ScreenAgent: A Vision Language Model-driven Computer Control Agent](https://mlsanity.com/paper/2402.07945/) (2024)
- [RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation](https://mlsanity.com/paper/2402.16667/) (2024)
- [Agent Planning with World Knowledge Model](https://mlsanity.com/paper/2405.14205/) (2024)
- [KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents](https://mlsanity.com/paper/2403.03101/) (2024)
- [FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents](https://mlsanity.com/paper/2406.14884/) (2024)
- [Benchmarking Agentic Workflow Generation](https://mlsanity.com/paper/2410.07869/) (2024)
- [MPO: Boosting LLM Agents with Meta Plan Optimization](https://mlsanity.com/paper/2503.02682/) (2025)
- [WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models](https://mlsanity.com/paper/2411.05451/) (2024)
- [From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents](https://mlsanity.com/paper/2603.22386/) (2026)
- [A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks](https://mlsanity.com/paper/2510.05608/) (2025)
- [Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents](https://mlsanity.com/paper/2311.13148/) (2023)
- [Automating the Enterprise with Foundation Models](https://mlsanity.com/paper/2405.03710/) (2024)
- [MAO: A Framework for Process Model Generation with Multi-Agent Orchestration](https://mlsanity.com/paper/2408.01916/) (2024)
- [Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Language Model Agents](https://mlsanity.com/paper/2404.06750/) (2024)
- [A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model](https://mlsanity.com/paper/2408.02920/) (2024)
- [Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration](https://mlsanity.com/paper/2410.22916/) (2024)
- [PolicyGuide: From Guarding One Action to Guiding the Whole Workflow for Policy-Compliant LLM Agents](https://mlsanity.com/paper/2608.19861/) (2026)
- [LawFlow: Collecting and Simulating Lawyers' Thought Processes on Business Formation Case Studies](https://mlsanity.com/paper/2504.18942/) (2025)
- [The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Products](https://mlsanity.com/paper/2604.00053/) (2026)
- [Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows](https://mlsanity.com/paper/2509.04198/) (2025)
- [POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation](https://mlsanity.com/paper/2601.11816/) (2026)
- [Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges](https://mlsanity.com/paper/2605.02592/) (2026)
- [Software Engineering for and with GUI Agent](https://mlsanity.com/paper/2608.09278/) (2026)
- [Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution](https://mlsanity.com/paper/2607.14456/) (2026)

## Abstract (arXiv)

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.