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.

Next activity prediction is one of the main tasks of Predictive Process Monitoring (PPM), enabling organizations to forecast the execution of business processes and respond accordingly. Deep learning models are effective at predictions, but with the price of intensive training and feature engineering, rendering them less generalizable across domains. Large Language Models (LLMs) have been recently suggested as an alternative, but their capabilities in Process Mining tasks are still to be extensively investigated. This work introduces a framework leveraging LLMs and Retrieval-Augmented Generation to enhance their capabilities for predicting next activities. By leveraging sequential information and data attributes from past execution traces, our framework enables LLMs to make more accurate predictions without additional training. We evaluate the approach on a wide range of event logs and compare it with state-of-the-art techniques. Findings show that our framework achieves competitive performance while being more adaptable across domains. Moreover, we assess early prediction capabilities, validate the significance of observed differences through statistical testing, and explore the impact of fine-tuning. Despite these advantages, we also report the framework’s limitations, mainly related to interleaving activity sensitivity and concept drifts. Our findings highlight the potential of retrieval-augmented LLMs in PPM while identifying the need for future research into handling evolving process behaviors and the development of standard benchmarks.

Abstract Traditionally, process-aware Decision Support Systems (DSSs) have been enhanced with AI functionalities to facilitate quick and informed decision-making. In this context, AI-Augmented Business Process Management Systems have emerged as innovative human-centric information systems, blending flexibility, autonomy, and conversational capability. Large Language Models (LLMs) have significantly boosted such systems, showcasing remarkable natural language processing capabilities across various tasks. Despite the potential of LLMs to support human decisions in business contexts, empirical validations of their effectiveness for process-aware decision support are scarce in the literature. In this paper, we propose the Business Process Large Language Model (BPLLM) framework, a novel approach for enacting actionable conversations with human workers. BPLLM couples Retrieval-Augmented Generation with fine-tuning, to enrich process-specific knowledge. Additionally, a process-aware chunking approach is incorporated to enhance the BPLLM pipeline. We evaluated the approach in various experimental scenarios to assess its ability to generate accurate and contextually relevant answers to users’ questions. The empirical study shows the promising performance of the framework in identifying the presence of particular activities and sequence flows within the considered process model, offering insights into its potential for enhancing process-aware DSSs.