A Formal Framework for Declarative Agentic AI in Business Process Analysis
For researchers and practitioners in business process management, this provides a formal foundation for agentic AI integration, though it is an incremental contribution combining existing concepts.
The paper introduces a formal framework (AGO methodology) for agentic business process analysis, grounded in set theory and logic, enabling structured querying, incremental updates, and automatic workflow generation with soundness and completeness guarantees.
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.