Agentic AI opens new opportunities for automating Business Processes (BP), enabling autonomous decision-making and dynamic adaptation. However, realizing 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.
The AGO methodology captures the modeling 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, organizing all definitions into a Business Process 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.
Blogger's Review: This paper provides a rigorous framework that combines mathematical logic and set theory, enhancing the automation and adaptability of business process analysis, showcasing the immense potential of Agentic AI in practical applications.