mlsanityData to 7 Oct 2026

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

arXiv 2504.03693 ↗First posted 23 Mar 2025cs.SE cs.MAcited by 10

On the map: LLM agents and tool use → Software engineering coding agents

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.

Key facts. Cited by 10 papers in the map (data through 7 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.

Builds on

6 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Re-Thinking Process Mining in the AI-Based Agents Era14 Aug

Other references · 5

  1. 2022AI-Augmented Business Process Management Systems: A Research Manifestocited by 27
  2. 2022Agent Miner: An Algorithm for Discovering Agent Systems from Event Datacited by 9
  3. 2024AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulationcited by 8
  4. 2017Blockchains for Business Process Management - Challenges and Opportunitiescited by 5
  5. 2023Large Process Models: A Vision for Business Process Management in the Age of Generative AIcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 9.

10 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Process Constitutions and Process Stewards: Towards the Next Generation of BPM for Agentic Organizations
  2. 2026Agent Behavior Mining: Generative AI Agent Governance in Business Processes
  3. 2026Governing the Agentic Enterprise: A Governance Maturity Model for Managing AI Agent Sprawl in Business Operations
  4. 2025With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems
  5. 2026A Practical Guide to Agentic AI Transition in Organizations
  6. 2025Perceptions of Agentic AI in Organizations: Implications for Responsible AI and ROI
  7. 2026Agentic AI in Industry: Adoption Level and Deployment Barriers
  8. 2026Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities