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ProAgent: From Robotic Process Automation to Agentic Process Automation (2311.10751v2)

Published 2 Nov 2023 in cs.RO, cs.AI, and cs.CL
ProAgent: From Robotic Process Automation to Agentic Process Automation

Abstract: 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 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.

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Overview of "ProAgent: From Robotic Process Automation to Agentic Process Automation"

The paper "ProAgent: From Robotic Process Automation to Agentic Process Automation" introduces Agentic Process Automation (APA) as a novel paradigm to address the limitations of traditional Robotic Process Automation (RPA). The focus is on integrating LLMs to facilitate more intelligent automation through dynamic decision-making and workflow construction.

Introduction

The evolution of automation technology, from simple mechanical devices to digital automation, has continually aimed to reduce human intervention in various processes. RPA, predominating in the digital era, excels in handling repetitive, rule-based tasks but falls short when complexity or human-like intelligence is required. With the emergence of LLMs, the paper proposes APA to overcome these limitations using LLM-based agents for enhanced automation.

Methodology

The paper highlights two primary advancements in APA:

  1. Agentic Workflow Construction: LLM-based agents autonomously construct workflows based on human instructions. The proposed "Agentic Workflow Description Language" employs JSON structure for data flow and Python code for control flow, leveraging LLMs' pre-trained understanding of programming concepts.
  2. Agentic Workflow Execution: This involves the use of DataAgent and ControlAgent to make dynamic decisions during workflow execution. DataAgent addresses complex data processing tasks, while ControlAgent takes over intricate decision-making processes, thus enhancing flexibility in workflow automation.

ProAgent Implementation

ProAgent exemplifies APA's framework by integrating the agent-driven workflow construction and execution within a unified architecture. Key aspects include:

  • Workflow Description Language: Utilizing JSON and Python, the language supports creating complex and dynamic workflows that LLMs can easily interpret and generate.
  • Dynamic Decision-Making with Agents: ProAgent incorporates DataAgent for data-intensive tasks and ControlAgent for decision-critical workflow branches.
  • Workflow Construction Process: The iterative process involves defining and implementing actions, orchestrating workflows, and validating through dynamic testing.

Experimentation and Results

Through proof-of-concept experiments on the n8n platform, ProAgent successfully demonstrates its capability to generate and execute workflows from human instructions. The empirical results underscore the feasibility of APA, demonstrating how ProAgent dynamically constructs workflows and integrates agents for complex task management.

Implications and Discussion

The introduction of APA and ProAgent has both theoretical and practical implications:

  • Relation to Tool Learning: ProAgent embodies an integration of tool utilization and creation, offering a novel approach by generating complex workflows as composite tools.
  • Process Mining Synergy: The integration of Process Mining can optimize workflow construction and provide insights into workflow efficiency, leading to continuous improvement.
  • Addressing Automation Bias: As automation technologies embed more intelligence, the risk of automation bias intensifies. The development of robust, interpretable, and safe APA frameworks is essential.

Conclusion

The paper contributes significantly to the evolving field of automation by leveraging LLMs for intelligent process automation. ProAgent is pioneering in its approach to offload intelligent tasks traditionally requiring human oversight, offering a glimpse into the future of automated industrial and corporate workflows. As technology advances, APA could redefine the landscape of automation, making it more efficient, adaptable, and intelligent.

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Authors (12)
  1. Yining Ye (11 papers)
  2. Xin Cong (46 papers)
  3. Shizuo Tian (4 papers)
  4. Jiannan Cao (9 papers)
  5. Hao Wang (1119 papers)
  6. Yujia Qin (41 papers)
  7. Yaxi Lu (12 papers)
  8. Heyang Yu (6 papers)
  9. Huadong Wang (15 papers)
  10. Yankai Lin (125 papers)
  11. Zhiyuan Liu (433 papers)
  12. Maosong Sun (337 papers)
Citations (15)
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