{"ID":2897547,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00844","arxiv_id":"2508.00844","title":"Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework","abstract":"Artificial intelligence (AI) systems are evolving beyond passive tools into autonomous agents capable of reasoning, adapting, and acting with minimal human intervention. Despite their growing presence, a structured framework is lacking to classify and compare these systems. This paper develops a typology of agentic AI systems, introducing eight dimensions that define their cognitive and environmental agency in an ordinal structure. Using a multi-phase methodological approach, we construct and refine this typology, which is then evaluated through a human-AI hybrid approach and further distilled into constructed types. The framework enables researchers and practitioners to analyze varying levels of agency in AI systems. By offering a structured perspective on the progression of AI capabilities, the typology provides a foundation for assessing current systems and anticipating future developments in agentic AI.","short_abstract":"Artificial intelligence (AI) systems are evolving beyond passive tools into autonomous agents capable of reasoning, adapting, and acting with minimal human intervention. Despite their growing presence, a structured framework is lacking to classify and compare these systems. This paper develops a typology of agentic AI...","url_abs":"https://arxiv.org/abs/2508.00844","url_pdf":"https://arxiv.org/pdf/2508.00844v1","authors":"[\"Christopher Wissuchek\",\"Patrick Zschech\"]","published":"2025-07-07T14:05:30Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.ET\",\"cs.MA\",\"econ.GN\"]","methods":"[]","has_code":false}