Agentic Business Process Management Systems

TL;DR

This paper discusses the emergence of Agentic Business Process Management Systems (A-BPMS), which leverage AI, process mining, and autonomy to transform process management from automation to data-driven, autonomous systems.

Contribution

It proposes an architectural vision for A-BPMS that integrates autonomy, reasoning, and learning, redefining process automation and governance boundaries.

Findings

Process mining enables agents to sense and reason about process states.

A-BPMS supports a continuum from human-driven to fully autonomous processes.

The architecture facilitates autonomous decision-making and process optimization.

Abstract

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.