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.

翻译：自20世纪90年代初以来，业务流程管理（BPM）学科的发展始终伴随着自动化技术的迭代浪潮。其中部分技术实现了对独立任务的自动化，而另一些则专注于端到端流程执行的编排。生成式与智能体化人工智能（AI）的兴起正开启新一轮技术浪潮。然而，此次浪潮将呈现本质性差异：其焦点从自动化转向自主化，从设计驱动的业务流程管理转向数据驱动的管理模式，并深度融合流程挖掘技术。本立场论文基于2025年"AI for BPM"研讨会主题报告，阐释了流程挖掘如何为智能体构建感知流程状态、推理优化契机、执行维护与优化操作的基础能力。论文提出智能体化业务流程管理系统（A-BPMS）的架构愿景：这类新型平台将自主性、推理能力与学习机制融入流程管理与执行体系。文中论证此类系统需支持从人工驱动到完全自主的连续流程谱系，从而重新定义流程自动化与治理的边界。