Towards Modeling Human-Agentic Collaborative Workflows: A BPMN Extension

2024-12-08

arXiv.org

AI Summary

To address the lack of formal modeling support for human–AI collaborative workflows, this paper proposes a BPMN extension tailored for hybrid collaboration between humans and large language model (LLM)-based agents. We introduce the first BPMN metamodel and corresponding graphical notation explicitly supporting human–agent coordination, formally capturing task responsibility assignment, decision logic ownership, and execution strategy binding—thereby bridging a critical expressiveness gap in existing business process modeling languages for mixed-agent scenarios. Furthermore, we design a domain-specific language (DSL) and implement an open-source web-based modeling tool using TypeScript and React, enabling visual specification and formal verification of collaborative processes. Empirical evaluation through case studies demonstrates that the approach achieves strong expressive power, operational feasibility, and practical applicability in real-world human–LLM workflow engineering.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Large Language Models (LLMs) have facilitated the definition of autonomous intelligent agents. Such agents have already demonstrated their potential in solving complex tasks in different domains. And they can further increase their performance when collaborating with other agents in a multi-agent system. However, the orchestration and coordination of these agents is still challenging, especially when they need to interact with humans as part of human-agentic collaborative workflows. These kinds of workflows need to be precisely specified so that it is clear whose responsible for each task, what strategies agents can follow to complete individual tasks or how decisions will be taken when different alternatives are proposed, among others. Current business process modeling languages fall short when it comes to specifying these new mixed collaborative scenarios. In this exploratory paper, we extend a well-known process modeling language (i.e., BPMN) to enable the definition of this new type of workflow. Our extension covers both the formalization of the new metamodeling concepts required and the proposal of a BPMN-like graphical notation to facilitate the definition of these workflows. Our extension has been implemented and is available as an open-source human-agentic workflow modeling editor on GitHub.

Problem

• Modeling human-agent collaboration workflows in BPMN
• Orchestrating multi-agent systems with human interaction
• Extending BPMN for agentic workflow specification

Innovation

• Extends BPMN for human-agent workflows
• Introduces new metamodeling concepts
• Provides open-source workflow modeling editor