Agentic artificial intelligence: Principles and scope
Keywords:
artificial intelligence, agentic, autonomous systems, decision-making, adaptive behaviorSynopsis
Artificial intelligence is undergoing a profound transformation. Originally conceived as a set of techniques oriented toward information processing and task automation, it has evolved into systems capable of organizing behavior, making decisions, and acting autonomously within dynamic environments. This shift has given rise to a new paradigm: agentic artificial intelligence, whose value lies in redirecting the analytical focus from the generation of outputs to the coherent structuring of action over time.
The significance of this work resides in its engagement with a critical gap in contemporary scholarship. While there is an abundance of studies on generative artificial intelligence and machine learning, the development of frameworks capable of explaining how systems integrate perception, decision-making, and action within a unified operational logic remains limited. Prevailing approaches tend to emphasize what systems produce, rather than how they organize their behavior or how such behavior ought to be evaluated. This book addresses this limitation by advancing an understanding of artificial intelligence centered on the organization of behavior, thereby enabling a more comprehensive analysis of the nature, functioning, and implications of intelligent systems.
Within this context, Agentic Artificial Intelligence: Principles and Scope seeks to develop a conceptual, structural, and operational framework that enables the rigorous and integrative understanding, design, implementation, and evaluation of agentic systems. The work advances an epistemological reconfiguration in which intelligence is no longer conceived as a capacity for calculation or generation, but rather as the ability to sustain coherent, continuous, and adaptive behavior in complex environments. This perspective is particularly pertinent in a context where intelligent systems increasingly acquire autonomy and actively participate in organizational, economic, and social processes.
The book is addressed to an interdisciplinary audience: researchers and scholars in search of advanced theoretical frameworks; graduate students interested in conducting rigorous research; professionals and decision-makers seeking to understand the implications of agentic AI for productivity, competitiveness, and governance; and designers of intelligent systems requiring robust criteria for constructing more coherent and autonomous architectures. The structure of the book follows a progressive and systematic logic.
Chapter 1, Evolution and Conceptual Foundations of Agentic AI, establishes the theoretical underpinnings by examining the nature of agency, its operational dynamics, and its conceptual boundaries. It further explores the emergence of the agentic paradigm and its ontological differentiation, emphasizing properties such as structural coherence, operational continuity, and adaptability.
Chapter 2, Architecture and Structuring of Agentic AI, addresses the internal organization of the agent, analyzing components such as perception, decision, action, and memory, as well as their architectural configurations, including multi-agent systems and distributed structures.
Chapter 3, Design of Agentic AI, develops construction principles grounded in the organization of behavior, action modeling, and functional integration, providing criteria for the design of robust and coherent systems.
Chapter 4, Implementation of Agentic Systems, shifts the analysis to the practical domain, addressing the agent lifecycle, technological integration, operation in real-world environments, performance evaluation, and risk management.
Chapter 5, Structural Measurement of Agentic AI, introduces innovative evaluation criteria based on coherence, continuity, and autonomy, enabling the assessment of agentic behavior beyond outcome-based metrics.
Chapter 6, Impact, Governance, and Future, examines the social, economic, and organizational implications of agentic AI, as well as the associated ethical and regulatory challenges and prospective scenarios of its evolution.
Finally, Chapter 7, Final Reflection, synthesizes the principal contributions of the work, consolidating a critical perspective on agentic artificial intelligence as an emergent paradigm.
In summary, this work is significant in that it provides a comprehensive framework for understanding artificial intelligence in its most advanced phase, centered on the organization of behavior. Its contribution extends beyond the description of technologies, offering a foundation for understanding how intelligent systems act, how they should be designed, and how they ought to be evaluated and governed. In an environment characterized by complexity and increasing technological autonomy, this approach proves indispensable for advancing toward a more rigorous, coherent, and responsible development of artificial intelligence.
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References
Abou Ali, M., Dornaika, F., & Charafeddine, J. (2026). Agentic AI: A comprehensive survey of architectures, applications, and future directions. Artificial Intelligence Review, 59(11). https://doi.org/10.1007/s10462-025-11422-4
Acemoglu, D., & Restrepo, P. (2019). The wrong kind of AI? Artificial intelligence and the future of labor demand. Cambridge Journal of Economics, 44(1), 25–55. https://economics.mit.edu/sites/default/files/publications/The%20Wrong%20Kind%20of%20AI%20-%20Artificial%20Intelligence%20and.pdf
Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: Autonomous intelligence for complex goals—A comprehensive survey. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3532853
Adabara, I., Olaniyi, S. B., Nuhu, S. A., et al. (2025). Trustworthy agentic AI systems: A cross-layer review of architectures, threat models, and governance strategies for real-world deployment. F1000Research, 14, 905. https://doi.org/10.12688/f1000research.169927.1
Adadi, A., & Berrada, M. (2018). Peeking inside the black box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
Alqurni, J. (2026). Exploring the role of agentic AI in fostering self-efficacy, autonomy support, and self-learning motivation in higher education. Frontiers in Artificial Intelligence, 9. https://doi.org/10.3389/frai.2026.1738774
Amancharla, C., Shyamsundar, S., Radhakrishnan, S., & Gupta, U. (2025). Agentic AI: From expectations to execution—Building autonomous, resilient and intelligent agentic AI systems. Infosys. https://www.infosys.com/techcompass/documents/building-autonomous-aria-systems.pdf
Ng, A. (2018). Machine learning yearning: Technical strategy for AI engineers in the era of deep learning. Deeplearning.AI. https://www.deeplearning.ai/machine-learning-yearning/
Baber, C., Kandola, P., Apperly, I., & McCormick, E. (2025). Human-centred explanations for artificial intelligence systems. Ergonomics, 68(3), 391–405. https://doi.org/10.1080/00140139.2024.2334427
Balaji, P. G., & Srinivasan, D. (2010). An introduction to multi-agent systems. In D. Srinivasan & L. C. Jain (Eds.), Innovations in multi-agent systems and applications – 1 (Vol. 310). Springer. https://doi.org/10.1007/978-3-642-14435-6_1
Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025). The rise of agentic AI: A review of definitions, frameworks, architectures, applications, evaluation metrics, and challenges. Future Internet, 17(9), 404. https://doi.org/10.3390/fi17090404
Biswas, A., & Talukdar, W. (2025). Building agentic AI systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt. Packt Publishing. https://www.packtpub.com/en-us/product/building-agentic-ai-systems-9781801079273
Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve. American Economic Journal: Macroeconomics, 13(1), 333–372. https://ide.mit.edu/sites/default/files/publications/2019-04JCurvebrief.final2_.pdf
Chan, A., Rahimi-Ardabilli, H., Rogers, W. A., & Coiera, E. (2025). The real-world impact of artificial intelligence ethics frameworks across a decade in healthcare: A scoping review. Journal of the American Medical Informatics Association, 32(11), 1767–1777. https://doi.org/10.1093/jamia/ocaf167
Chen, X., Xiang, J., Lu, S., Liu, Y., He, M., & Shi, D. (2025). Evaluating large language models and agents in healthcare: Key challenges in clinical applications. Intelligent Medicine, 5(2), 151–163. https://doi.org/10.1016/j.imed.2025.03.001
Cockburn, I. M., Henderson, R. M., & Stern, S. (2018). The impact of artificial intelligence on innovation (NBER Working Paper No. 24449). https://doi.org/10.3386/w24449
Coeckelbergh, M. (2020). AI ethics. MIT Press. https://doi.org/10.7551/mitpress/12549.001.0001
Collaco, B. G., et al. (2026). The role of agentic artificial intelligence in healthcare: A scoping review. NPJ Digital Medicine. https://doi.org/10.1038/s41746-026-02517-5
Dorri, A., Kanhere, S. S., & Jurdak, R. (2018). Multi-agent systems: A survey. IEEE Access, 6, 28573–28593. https://doi.org/10.1109/ACCESS.2018.2831228
Du, S., et al. (2026). Optimization of large language model-based agents. ACM Computing Surveys. https://doi.org/10.1145/3789261
Durga, R. K. (2025). A comprehensive study of agentic AI systems. International Journal of Scientific Research and Modern Technology, 4(5), 157–164. https://doi.org/10.38124/ijsrmt.v4i5.1043
European Commission. (2024). Proposal for a regulation laying down harmonised rules on artificial intelligence (AI Act). https://eur-lex.europa.eu/legal-content/ES/TXT/PDF/?uri=OJ:L_202401689
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8cd550d1
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2021). Achieving a “good AI society”: Comparing the aims and progress of the EU and the US. Science and Engineering Ethics, 27, 68. https://doi.org/10.1007/s11948-021-00338-3
Frank, M. R., et al. (2025). AI exposure predicts unemployment risk: A new approach to assessing the labor-market impacts of AI. PNAS Nexus. https://doi.org/10.1093/pnasnexus/pgaf107
Guidotti, R., et al. (2019). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 93. https://doi.org/10.1145/3236009
Hahn, M., Tretter, M., & Dabrock, P. (2026). Ethical perspectives on AI agents and agentic AI. AI Ethics, 6, 218. https://doi.org/10.1007/s43681-026-01027-0
Hofmann, P., et al. (2025). Conceptualizing the design space of AI strategy. Business & Information Systems Engineering. https://doi.org/10.1007/s12599-025-00856-1
Hosseini, S., & Seilani, H. (2025). The role of agentic AI in shaping a smart future: A systematic review. Array. https://doi.org/10.1016/j.array.2025.100399
Jobin, A., Ienca, M., & Vayena, E. (2020). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399.
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes. Business & Information Systems Engineering, 63, 5–20. https://doi.org/10.1007/s12599-020-00676-7
Karnouskos, S., et al. (2020). Industrial agents as enablers of Industry 4.0. IEEE Industrial Electronics Magazine, 14(3), 18–32. https://doi.org/10.1109/MIE.2019.2962225
Lewis, P. R., & Sarkadi, Ş. (2024). Reflective artificial intelligence. Minds and Machines, 34, 14. https://doi.org/10.1007/s11023-024-09620-3
Li, H. (2026). General framework of AI agents. Journal of Computer Science and Technology. https://doi.org/10.1007/s11390-025-5951-5
Maldonado, D., et al. (2024). Multi-agent systems survey. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3409051
Mantia, L., et al. (2025). Designing a successful agentic AI system. Harvard Business Review. https://hbr.org/2025/10/designing-a-successful-agentic-ai-system
Mejía Trejo, J. (2024). Inteligencia artificial: Fundamentos de ingeniería de prompts. AMIDI. https://amidibiblioteca.amidi.mx/index.php/AB/catalog/book/48
Mejía Trejo, J. (2025). Inteligencia artificial y su repercusión en la educación superior. AMIDI. https://amidibiblioteca.amidi.mx/index.php/AB/catalog/book/70
OpenAI. (s. f.). A practical guide to building agents. https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
OECD. (2022). OECD framework for the classification of AI systems. https://doi.org/10.1787/cb6d9eca-en
OECD. (2025a). Governing with artificial intelligence. https://www.oecd.org
OECD. (2025b). Artificial intelligence and competitive dynamics. https://www.oecd.org
OECD. (2025c). AI governance through global red lines. https://oecd.ai
OECD. (2026). The agentic AI landscape and its conceptual foundations. https://www.oecd.org
Ozdemir, S. (2026). Building agentic AI: Workflows, fine-tuning, optimization, and deployment. Pearson.
Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond. Applied Psychology. https://doi.org/10.1111/apps.12241
Piccialli, F., et al. (2025). AgentAI in Industry 4.0. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2025.128404
Poole, D. L., & Mackworth, A. K. (2017). Artificial intelligence: Foundations of computational agents (2nd ed.). Cambridge University Press. https://doi.org/10.1017/9781108164085
Rahwan, I., et al. (2019). Machine behaviour. Nature, 568, 477–486. https://doi.org/10.1038/s41586-019-1138-y
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? KDD. https://doi.org/10.1145/2939672.2939755
Russell, S. (2019). Human compatible. Viking.
Russell, S., & Norvig, P. (2022). Artificial intelligence: A modern approach (4th ed.). Pearson.
Sapkota, R., et al. (2026). AI agents vs. agentic AI. Information Fusion. https://doi.org/10.1016/j.inffus.2025.103599
Stone, P., et al. (2021). Artificial intelligence and life in 2030. Stanford University.
United Nations. (2024). Governing AI for humanity. https://www.un.org
UNESCO. (2025a). Artificial intelligence and education. https://unesdoc.unesco.org
UNESCO. (2025b). Artificial intelligence and culture. https://www.unesco.org
Vinuesa, R., et al. (2020). The role of artificial intelligence in achieving SDGs. Nature Communications, 11, 233. https://doi.org/10.1038/s41467-019-14108-y
Wang, Z., et al. (2024). Explainable AI roadmap. ACM Transactions on Autonomous and Adaptive Systems. https://doi.org/10.1145/3702004
Wang, L., et al. (2024). LLM-based autonomous agents. Frontiers of Computer Science. https://doi.org/10.1007/s11704-024-40231-1
World Bank. (2026). World development report 2026. https://www.worldbank.org
World Economic Forum. (2024). Navigating the AI frontier. https://www.weforum.org
World Economic Forum. (2025). AI agents in action. https://www.weforum.org
World Health Organization. (2021). Ethics and governance of AI for health. https://iris.who.int
Yadav, P., et al. (2023). Multi-agent reinforcement learning. Sensors, 23(10), 4710. https://doi.org/10.3390/s23104710
Yuan, Y., & Xie, T. (2026). Reinforce LLM reasoning. ICML Proceedings. https://proceedings.mlr.press
Zhang, Z., et al. (2025). Memory mechanisms in LLM agents. ACM TOIS. https://doi.org/10.1145/3748302
Xie, H., et al. (2025). AI for structural design automation. Automation in Construction. https://doi.org/10.1016/j.autcon.2025.106496
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