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 view on business processes. This shift is driven by the realization of process awareness by agent-oriented abstractions: software and human agents act as primary functional entities that perceive, reason, and act within explicit process frames. Thus, APM moves away from automation-oriented BPM towards systems in which autonomy is constrained, aligned, and made operational through process aware agents. We introduce the core abstractions and architectural elements required to realize APM systems and elaborate on four key capabilities that agents in APM systems 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.

The Business Process Modeling Notation (BPMN) is the de facto standard for business process modeling. While widely adopted for its intuitive graphical notation, its execution semantics described in natural language lacks a commonly agreed formal foundation, leading to variability in execution across different BPM systems (BPMSs) and increasing the risk of creating models with semantic errors costly to correct at runtime. Although many formalisms have been used to model portions of BPMN, their reasoning capabilities are mostly restricted to control-flow, making them unsuitable for semantic analysis where data and global exception handling play a central role in execution. To address this, we propose a formalization from BPMN to ConGolog, a logical concurrent processes language based on the Situation Calculus, for representing and reasoning about dynamic domains. A major innovation is using ConGolog to rigorously capture the semantics of BPMN global exceptions. Our framework supports advanced reasoning, allowing for semantic analysis of BPMN models before execution to predict runtime errors within a safe simulation setting, while laying the foundation for reasoning layers in next-generation AI-augmented BPMSs. We validate the approach through a prototype and comprehensive evaluation, demonstrating the computational feasibility of the translation and the semantic correctness of reasoning tasks.

Process mining (PM)-based goal recognition (GR) techniques, which infer goals or targets based on sequences of observed actions, have shown efficacy in real-world engineering applications. This study explores the applicability of PM-based GR in identifying target poses for users employing powered transhumeral prosthetics. These prosthetics are designed to restore missing anatomical segments below the shoulder, including the hand. In this article, we aim to apply the GR techniques to identify the intended movements of users, enabling the motors on the powered transhumeral prosthesis to execute the desired motions precisely. In this way, a powered transhumeral prosthesis can assist individuals with disabilities in completing movement tasks. PM-based GR techniques were initially designed to infer goals from sequences of observed actions, where discrete event names represent actions. However, the electromyography electrodes and kinematic sensors on powered transhumeral prosthetic devices register sequences of continuous, real-valued data measurements. Therefore, we rely on methods to transform sensor data into discrete events and integrate these methods with the PM-based GR system to develop target pose recognition approaches. Two data transformation approaches are introduced. The first approach relies on the clustering of data measurements collected before the target pose is reached (the clustering approach). The second approach uses the time series of measurements collected while the dynamic user movement to perform linear discriminant analysis (LDA) classification and identify discrete events (the dynamic LDA approach). These methods are evaluated through offline and human-in-the-loop (online) experiments and compared with established techniques, such as static LDA, an LDA classification based on data collected at static target poses, and GR approaches based on neural networks. Real-time human-in-the-loop experiments further validate the effectiveness of the proposed methods, demonstrating that PM-based GR using the dynamic LDA classifier achieves superior F 1 score and balanced accuracy compared to state-of-the-art techniques.

A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses.

The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex knowledge-intensive processes in such domains. A knowledge-intensive process is influenced by user decision making and coupled with contextual data and knowledge production, and involves performing complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and knowledge-intensive processes must be robust to unexpected conditions and adaptable to unanticipated exceptions, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. To tackle this issue, in this paper we present SmartPM, a model and a prototype Process Management System featuring a set of techniques providing support for automated adaptation of knowledge-intensive processes at runtime. Such techniques are able to automatically adapt process instances when unanticipated exceptions occur, without explicitly defining policies to recover from exceptions and without the intervention of domain experts at runtime, aiming at reducing error-prone and costly manual ad-hoc changes, and thus at relieving users from complex adaptations tasks. To accomplish this, we make use of well-established techniques and frameworks from Artificial Intelligence, such as situation calculus, IndiGolog and classical planning. The approach, which is backed by a formal model, has been implemented and validated with a case study based on real knowledge-intensive processes coming from an emergency management domain.