Conceptualizing Cognitive and Agentic Digital Twins

Authors

Keywords:

Digital Twin, Cognitive Digital Twin, Agentic AI, Large Language Models, Cyber-Physical Systems, Semantic Interoperability, Multi-Agent Systems

Abstract

Digital Twins (DTs) have extended beyond the original concept of a static digital model, to a dynamic, data-driven, and increasingly intelligent cyber-physical structure, which underlies modern Industry 4.0 systems. The paper gives a comprehensive and integrative conceptualization of DTs, Cognitive Digital Twins (CDTs), and agentic AI-enhanced DTs through an organized narrative literature review which synthesizes the foundational definitions, the theoretical differences between DTs and Cyber-Physical Systems (CPS), and the evolution of Digital Models and Digital Shadows to fully synchronous, two-way DTs. The research synthesizes the major architectural models, such as layered, multi-dimensional, cognitive and agentic models, and discusses the facilitating technologies that enable real-time synchronization, semantic reasoning, and autonomous decision-making. The state-of-the-art analysis indicates that the key research trends include ecosystem-based DTs, AI-driven analytics, semantic enrichment, and the development of LLM-enabled agentic DTs that are able to plan and coordinate actions in a goal-oriented manner. The paper also compares industrial and open-source DT platforms, outlines the present-day limitations in semantic interoperability, cognitive integration, and autonomy and demonstrates real-world applications in manufacturing, healthcare, smart cities, and energy systems. The discussion proposes future research directions such as standardized cognitive layers, safe LLM grounding, multi-DT coordination, and governance frameworks of autonomous DT ecosystems. Overall, the paper contributes a unified conceptual model and a holistic synthesis that connects theory, architecture, technology, and application, providing a foundation for advancing the next generation of intelligent Digital Twin systems.

References

Adl, M. (2016). Cognitive Digital Twins: The next generation of simulation based systems engineering. Proceedings of the Conference on Systems Engineering Research.

Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. https://doi.org/10.1145/1721654.1721672

Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010

Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on Digital Twin: Definitions, characteristics, applications. IEEE Access, 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499

Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817–820. https://doi.org/10.1177/2399808318796416

Bazaz, S. M., Lohtander, M., & Varis, J. (2019). 5 Dimensional definition for a manufacturing Digital Twin. Procedia Manufacturing, 38, 1705–1712. https://doi.org/10.1016/j.promfg.2020.01.107

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209. https://doi.org/10.1007/s11036-013-0489-0

Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358

Glaessgen, E. H., & Stargel, D. S. (2012). The Digital Twin paradigm for future NASA and U.S. Air Force vehicles. AIAA Conference Proceedings. https://doi.org/10.2514/6.2012-1818

Gill H. (2008) “NSF perspective and status on cyber–physical systems” NSF Workshop on Cyber–physical Systems, Oct 16–17; Austin, TX, USA. Alexandria: National Science Foundation.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. ISBN 9780262035613

Grieves, M. W. (2002, October 31). Completing the cycle: Using PLM information in the sales and service functions [Conference presentation]. SME Management Forum, Troy, MI, United States.

Grieves, M. W. (2014). Digital Twin: Manufacturing excellence through virtual factory replication [White paper]. Dassault Systèmes. https://www.3ds.com/fileadmin/PRODUCTS-SERVICES/DELMIA/PDF/Whitepaper/DELMIA-APRISO-Digital-Twin-Whitepaper.pdf

Hasan, A., & Nguyen, D. T. (2026). Integrating agentic AI and digital twins for intelligent decision making systems. Array, 29, 100721. https://doi.org/10.1016/j.array.2026.100721

Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474

Liu, Y., Ji, T., Guo, X., Xu, X., & Polzer, J. (2025). A survey of cognitive digital twin and the potential use of LLMs. Manufacturing Letters, 44(Suppl.), 1242–1253. https://doi.org/10.1016/j.mfglet.2025.06.144

Lu, Y., Liu, C., Wang, K. I. K., Huang, H., & Xu, X. (2020). Digital Twin driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer Integrated Manufacturing, 61, 101837. https://doi.org/10.1016/j.rcim.2019.101837

Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital Twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198

Tao, F., & Zhang, M. (2017). Digital Twin shop floor: A new shop floor paradigm towards smart manufacturing. IEEE Access, 5, 20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital Twin in industry: State of the art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186

Zhang, Y., Qiu, M., Tsai, C., Hassan, M. M., & Alamri, A. (2017). Health CPS: Healthcare cyber physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88–95. https://doi.org/10.1109/JSYST.2015.2460747

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2026-05-31

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