Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916877075218432 |
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| author | Wissuchek, Christopher Zschech, Patrick |
| author_facet | Wissuchek, Christopher Zschech, Patrick |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_00844 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework Wissuchek, Christopher Zschech, Patrick Artificial Intelligence Emerging Technologies Multiagent Systems General Economics Economics 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. |
| title | Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework |
| topic | Artificial Intelligence Emerging Technologies Multiagent Systems General Economics Economics |
| url | https://arxiv.org/abs/2508.00844 |