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The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges

Author

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  • Ajay Bandi

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA)

  • Bhavani Kongari

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA
    These authors contributed equally to this work. The authors are listed alphabetically by last name.)

  • Roshini Naguru

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA
    These authors contributed equally to this work. The authors are listed alphabetically by last name.)

  • Sahitya Pasnoor

    (Independent Researcher, Omaha, NE 68022, USA
    These authors contributed equally to this work. The authors are listed alphabetically by last name.)

  • Sri Vidya Vilipala

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA
    These authors contributed equally to this work. The authors are listed alphabetically by last name.)

Abstract

Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems by bringing together their definitions, frameworks, and architectures, and by comparing them with related areas like generative AI, autonomic computing, and multi-agent systems. To do this, we reviewed 143 primary studies on current LLM-based and non-LLM-driven agentic systems and examined how they support planning, memory, reflection, and goal pursuit. Furthermore, we classified architectural models, input–output mechanisms, and applications based on their task domains where agentic AI is applied, supported using tabular summaries that highlight real-world case studies. Evaluation metrics were classified as qualitative and quantitative measures, along with available testing methods of agentic AI systems to check the system’s performance and reliability. This study also highlights the main challenges and limitations of agentic AI, covering technical, architectural, coordination, ethical, and security issues. We organized the conceptual foundations, available tools, architectures, and evaluation metrics in this research, which defines a structured foundation for understanding and advancing agentic AI. These findings aim to help researchers and developers build better, clearer, and more adaptable systems that support responsible deployment in different domains.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:404-:d:1742615
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    References listed on IDEAS

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    1. Yu, Jiangbo, 2025. "Preparing for an agentic era of human-machine transportation systems: Opportunities, challenges, and policy recommendations," Transport Policy, Elsevier, vol. 171(C), pages 78-97.
    2. Wünderlich, Nancy V. & Blut, Markus & Brock, Christian & Heirati, Nima & Jensen, Marcus & Paluch, Stefanie & Rötzmeier-Keuper, Julia & Tóth, Zsófia, 2025. "How to use emerging service technologies to enhance customer centricity in business-to-business contexts: A conceptual framework and research agenda," Journal of Business Research, Elsevier, vol. 192(C).
    3. Laurie Hughes & Yogesh K. Dwivedi & Keyao Li & Mandanna Appanderanda & Mousa Ahmad Al-Bashrawi & Inyoung Chae, 2025. "AI Agents and Agentic Systems: Redefining Global it Management," Journal of Global Information Technology Management, Taylor & Francis Journals, vol. 28(3), pages 175-185, July.
    4. Christopher Wissuchek & Patrick Zschech, 2025. "Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework," Papers 2508.00844, arXiv.org.
    5. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
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