A Formal Framework for Declarative Agentic AI in Business Process Analysis
Quick Answer
This paper introduces a formal framework for Agentic AI in Business Process analysis using the AGO methodology.
Quick Take
This paper introduces a formal framework for Agentic AI in Business Process analysis using the AGO methodology. It defines entities and interactions with precision, creating a Business Process Knowledge Base (BPKB) that supports structured querying and automatic workflow generation while ensuring soundness and completeness.
Key Points
- AGO methodology captures Agents, Goals, and Objects in Business Process modeling.
- The framework is grounded in set theory and mathematical logic.
- BPKB allows for structured querying and incremental updates.
- Automatic generation of Business Process workflows is supported.
- Ensures soundness and completeness of derived paths.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 15291v1 Announce Type: new Abstract: 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.
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