# Agentic Business Process Management Systems

arXiv: https://arxiv.org/abs/2601.18833 · HTML: https://mlsanity.com/paper/2601.18833/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes](https://mlsanity.com/paper/2504.03693/) (2025)

## Built on by (most cited first, 4 of 4)

- [Organizational Memory for Agentic Business Process Execution](https://mlsanity.com/paper/2607.03228/) (2026)
- [A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO](https://mlsanity.com/paper/2606.27188/) (2026)
- [DNative-Twin: Decision Graphs and Digital Twins for Reconstructable Agentic Decisions](https://mlsanity.com/paper/2609.03787/) (2026)
- [From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining](https://mlsanity.com/paper/2609.07984/) (2026)

## Abstract (arXiv)

Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orchestrating the execution of end-to-end processes. The rise of Generative and Agentic Artificial Intelligence (AI) is opening the way for another such wave. However, this wave is poised to be different because it shifts the focus from automation to autonomy and from design-driven management of business processes to data-driven management, leveraging process mining techniques. This position paper, based on a keynote talk at the 2025 Workshop on AI for BPM, outlines how process mining has laid the foundations on top of which agents can sense process states, reason about improvement opportunities, and act to maintain and optimize performance. The paper proposes an architectural vision for Agentic Business Process Management Systems (A-BPMS): a new class of platforms that integrate autonomy, reasoning, and learning into process management and execution. The paper contends that such systems must support a continuum of processes, spanning from human-driven to fully autonomous, thus redefining the boundaries of process automation and governance.