Agentic Business Process Management: A Research Manifesto
Authors: Diego Calvanese, Angelo Casciani, Giuseppe De Giacomo, Marlon Dumas, Fabiana Fournier, Timotheus Kampik, Emanuele La Malfa, Lior Limonad, Andrea Marrella, Andreas Metzger, Marco Montali, Daniel Amyot, Peter Fettke, Artem Polyvyanyy, Stefanie Rinderle-Ma, Sebastian Sardiña, Niek Tax, Barbara Weber
Organizations: Free University of Bozen-Bolzano, Bozen-Bolzano, Italy · Sapienza Università di Roma, Rome, Italy · University of Oxford, Oxford, United Kingdom · University of Tartu, Tartu, Estonia · IBM Research, Haifa, Israel · Umeå University, Umeå, Sweden · SAP, Berlin, Germany · paluno (Ruhr Institute for Software Technology), University of Duisburg Essen, Essen, Germany · University of Ottawa, Ottawa, Canada · German Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, Germany · Saarland University, 66123 Saarbrücken, Germany · The University of Melbourne, Parkville, VIC 3010, Australia · TUM School of Computation, Information, and Technology, TU Munich, Garching, Germany · RMIT University, Melbourne, Australia · Meta, London, United Kingdom · University of St. Gallen, St. Gallen, Switzerland
This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for governing autonomous agents executing processes in organizations. From a management perspective, APM represents a paradigm shift from the traditional process view of the business process, driven by the realization of process awareness and an agent-oriented abstraction, where software and human agents act as primary functional entities that perceive, reason, and act within explicit process frames. This perspective marks a shift from traditional, automation-oriented BPM toward systems in which autonomy is constrained, aligned, and made operational through process awareness. We introduce the core abstractions and architectural elements required to realize APM systems and elaborate on four key capabilities that such APM agents must support: framed autonomy, explainability, conversational actionability, and self-modification. These capabilities jointly ensure that agents' goals are aligned with organizational goals and that agents behave in a framed yet proactive manner in pursuing those goals. We discuss the extent to which the capabilities can be realized and identify research challenges whose resolution requires further advances in BPM, AI, and multi-agent systems. The manifesto thus serves as a roadmap for bridging these communities and for guiding the development of APM systems in practice.
Figures & tables
Figure 1: Conceptual architecture of an APM system depicting its key actors, components (boxes), and interactions (arrows). Process-awareness (red-colored) is realized by a framing mechanism that constrains and aligns agents at the macro level, while at the micro level each process-aware agent runs a Perceive–Reason–Act loop over framed knowledge and goals (its internal view of “the process” and its goals), as agents interact with other process-aware human- and AI-agents in the system, and with the external environment (external agents, objects, resources, AI models/techniques), using tools (messaging, sensors, services, and actuators).
Just like traditional BPM systems, agentic BPM systems are built around a specification of the process under consideration. Their distinguishing feature, however, is that the execution of the process is driven by multiple autonomous decision-makers, referred to as agents. Since such agents cannot be fully controlled, the process specification is augmented with explicit objectives, or goals, assigned to the participating agents. Agents then pursue these goals, at least to the best of their efforts, under suitable assumptions on the behavior of others, by adopting appropriate strategies. Centrally, the organization enacting the process can use these specifications to provide guardrails on the decision-making capabilities of agents at the strategy level. This paper sets up the mathematical foundations of such systems in three key settings and analyzes four foundational problems of agentic BPM.
Giuseppe De Giacomo, Timotheus Kampik, Lukas Kirchdorfer +2
University of Oxford, UK · SAP, Germany · Ume˚a University, Sweden +2
Business Process Management (BPM) was built on a foundational assumption that organizations are populated primarily by human actors whose work can be made visible, governable, and improvable through process models. That assumption is depreciating. AI agent ecosystems increasingly execute, coordinate, and adapt organizational work with limited human direction, challenging not only BPM's methods but its core conception of what a process is. We argue that BPM faces a constitutive shift from modeling human work to governing autonomous agents, for which we propose two new concepts: the Process Constitution, a machine-interpretable, value-laden framework that defines the space of admissible agent behavior, and the Process Steward, a governance agent that interprets and enforces it. The central value proposition of this new generation of BPM is not efficiency but organizational legibility: the capacity to keep agentic organizations accountable, contestable, and humanly understandable. We outline what this means and sketch the research agenda it opens.
Amin Jalali, Majid Rafiei
Department of Computer and Systems Sciences, Stockholm University, Sweden · SAP Signavio, Berlin, Germany
Agentic AI opens new opportunities for automating Business Process (BP), enabling autonomous decision-making and dynamic adaptation. However, realising this potential requires BP entities and their interactions to be defined with formal precision. This paper presents a formal framework for Agentic BP analysis through the AGO methodology. AGO captures the modelling perspective in terms of who is acting (Agents), why it is carried out (Goals), and what the relevant entities are (Objects). Grounded in set theory and mathematical logic, we formally define the AGO entity types and their interactions, organising all definitions into a BP Knowledge Base (BPKB). The resulting BPKB supports structured querying, incremental updates, and automatic generation of BP workflows, while ensuring soundness and completeness of the derived paths.
Mohammad Azarijafari, Luisa Mich, Michele Missikoff
Department of Industrial Engineering, University of Trento, Via Sommarive 9, 38123, Trento, Italy · Istituto di Analisi dei Sistemi ed Informatica (IASI) “Antonio Ruberti”, National Research Council (CNR), Via dei Taurini 19, 00185, Rome, Italy