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Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework

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  • Christopher Wissuchek
  • Patrick Zschech

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.

Suggested Citation

  • Christopher Wissuchek & Patrick Zschech, 2025. "Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework," Papers 2508.00844, arXiv.org.
    • Handle: RePEc:arx:papers:2508.00844
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    File URL: http://arxiv.org/pdf/2508.00844
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    References listed on IDEAS

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    1. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
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    Cited by:

    1. Ajay Bandi & Bhavani Kongari & Roshini Naguru & Sahitya Pasnoor & Sri Vidya Vilipala, 2025. "The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges," Future Internet, MDPI, vol. 17(9), pages 1-50, September.

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