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A Formal Framework for Declarative Agentic AI in Business Process Analysis

arXiv AI Archived Jun 16, 2026 ✓ Full text saved

arXiv: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 wh

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    Computer Science > Artificial Intelligence [Submitted on 13 Jun 2026] A Formal Framework for Declarative Agentic AI in Business Process Analysis Mohammad Azarijafari, Luisa Mich, Michele Missikoff 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.15291 [cs.AI]   (or arXiv:2606.15291v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.15291 Focus to learn more Submission history From: Mohammad Azarijafari [view email] [v1] Sat, 13 Jun 2026 13:12:16 UTC (197 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Jun 16, 2026
    Archived
    Jun 16, 2026
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