# Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes

arXiv: https://arxiv.org/abs/2504.03693 · HTML: https://mlsanity.com/paper/2504.03693/

## Key facts

- Cited by 10 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 6 of its 39 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [Agent Miner: An Algorithm for Discovering Agent Systems from Event Data](https://mlsanity.com/paper/2212.01454/) (2022)
- [AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation](https://mlsanity.com/paper/2408.08571/) (2024)
- [Blockchains for Business Process Management - Challenges and Opportunities](https://mlsanity.com/paper/1704.03610/) (2017)
- [Re-Thinking Process Mining in the AI-Based Agents Era](https://mlsanity.com/paper/2408.07720/) (2024)
- [Large Process Models: A Vision for Business Process Management in the Age of Generative AI](https://mlsanity.com/paper/2309.00900/) (2023)

## Built on by (most cited first, 10 of 10)

- [Agentic Business Process Management: A Research Manifesto](https://mlsanity.com/paper/2603.18916/) (2026)
- [Agentic Business Process Management Systems](https://mlsanity.com/paper/2601.18833/) (2026)
- [Formal Foundations of Agentic Business Process Management](https://mlsanity.com/paper/2604.17347/) (2026)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [PMAx: An Agentic Framework for AI-Driven Process Mining](https://mlsanity.com/paper/2603.15351/) (2026)
- [Organizational Memory for Agentic Business Process Execution](https://mlsanity.com/paper/2607.03228/) (2026)
- [The Integrator Advantage: Controlled Agentic AI for Small and Medium-Sized Companies](https://mlsanity.com/paper/2606.16649/) (2026)
- [Design and Implementation of Agentic Orchestrations and Orchestration of Agents](https://mlsanity.com/paper/2606.31518/) (2026)
- [Towards Agentic AI Governance: A Preliminary Assessment](https://mlsanity.com/paper/2607.07612/) (2026)
- [The Agentic Company OS: Substrate Inversion for Sustained Enterprise Agent Deployment](https://mlsanity.com/paper/2609.13334/) (2026)

## Abstract (arXiv)

With the rise of generative AI, industry interest in software agents is growing. Given the stochastic nature of generative AI-based agents, their effective and safe deployment in organizations requires robust governance, which can be facilitated by agentic business process management. However, given the nascence of this new-generation agent notion, it is not clear what BPM practitioners consider to be an agent, and what benefits, risks and governance challenges they associate with agent deployments. To investigate how organizations can effectively govern AI agents, we conducted a qualitative study involving semi-structured interviews with 22 BPM practitioners from diverse industries. They anticipate that agents will enhance efficiency, improve data quality, ensure better compliance, and boost scalability through automation, while also cautioning against risks such as bias, over-reliance, cybersecurity threats, job displacement, and ambiguous decision-making. To address these challenges, the study presents six key recommendations for the responsible adoption of AI agents: define clear business goals, set legal and ethical guardrails, establish human-agent collaboration, customize agent behavior, manage risks, and ensure safe integration with fallback options. Additionally, the paper outlines actions to align traditional BPM with agentic AI, including balancing human and agent roles, redefining human involvement, adapting process structures, and introducing performance metrics. These insights provide a practical foundation for integrating AI agents into business processes while preserving oversight, flexibility, and trust.