mlsanityData to 14 Oct 2026

Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework

arXiv 2508.00844 ↗First posted 7 Jul 2025cs.AI cs.ET cs.MAcited by 3

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 54 references that are in the map. First posted on arXiv 7 Jul 2025.

Builds on

12 of 54 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Understanding the planning of LLM agents: A survey5 Feb
  2. 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects7 Jan
  3. 2023Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures5 Oct
  4. 2023An In-depth Survey of Large Language Model-based Artificial Intelligence Agents23 Sep
  5. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
  6. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  7. 2023Harms from Increasingly Agentic Algorithmic Systems20 Feb

Other references · 5

  1. 2024Agent AI: Surveying the Horizons of Multimodal Interactioncited by 170
  2. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  3. 2023Generative AIcited by 120
  4. 2017Guidelines for including grey literature and conducting multivocal literature reviews in software engineeringcited by 41
  5. 2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?
  2. 2025Agentic AI Needs a Systems Theory
  3. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
  4. 2026Towards Agentic AI Governance: A Preliminary Assessment
  5. 2026Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions
  6. 2026Agentic Artificial Intelligence (AI): Architectures, Taxonomies, and Evaluation of Large Language Model Agents
  7. 2025Agentic AI and Multiagentic: Are We Reinventing the Wheel?
  8. 2025Distinguishing Autonomous AI Agents from Collaborative Agentic Systems: A Comprehensive Framework for Understanding Modern Intelligent Architectures