Agentic Digital Twins: Self-Evolving Models for Autonomous Systems
Keywords:
Autonomous systems, digital twins, real-time learning, machine learning, predictive maintenance, system performanceAbstract
The agentic digital twin can be a new frontier of autonomous systems capable of real-time learning, decision-making, and adaptation. These models, unlike traditional and static digital twins, are self-aware and learning because they autonomously interact with the environment to optimize performance. The paper will discuss their applications in robotics, Industrial automation, and custom healthcare. Robotics: In robotics, agentic digital twins would enable real-time optimization of production and reduce downtime. They improve the reliability of systems in industrial automation using predictive maintenance. Applied in the sphere of health, they enable adjusting the treatment plan dynamically, considering data regarding a patient, which enhances its outcomes. The paper discusses the case studies and performance measurements, providing the advantages and pitfalls of the inclusion of agentic digital twins. Results have indicated major positive changes in efficiency, flexibility, and scalability, which is a bright future in autonomous technologies.
References
Agrawal, A., Singh, V., & Fischer, M. (2022). A new perspective on digital twins: Imparting intelligence and agency to entities. IEEE Journal of Radio Frequency Identification, 6, 871-875. https://doi.org/10.1109/JRFID.2022.3225741
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7, 167653-167671. https://doi.org/10.1109/ACCESS.2019.2953499
Chen, D., & Lv, Z. (2022). Artificial intelligence enabled digital twins for training autonomous cars. Internet of Things and Cyber-Physical Systems. https://doi.org/10.1016/j.iotcps.2022.05.001
Liu, Y., Xu, H., Liu, D., & Wang, L. (2022). A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping. Robotics and Computer-Integrated Manufacturing, 78, 102365. https://doi.org/10.1016/j.rcim.2022.102365
Matthews, G., Hancock, P. A., Lin, J., Panganiban, A. R., Reinerman-Jones, L. E., Szalma, J. L., & Wohleber, R. W. (2021). Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems. Personality and Individual Differences, 169, 109969. https://doi.org/10.1016/j.paid.2020.109969
Mihai, S., et al. (2022). Digital twins: A survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255-2291. https://doi.org/10.1109/COMST.2022.3208773
Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021). The role of AI, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities. IEEE Access, 9, 32030-32052. https://doi.org/10.1109/ACCESS.2021.3060863
Schwartz, S. M., Wildenhaus, K., Bucher, A., & Byrd, B. (2020). Digital twins and the emerging science of self: Implications for digital health experience design and “small” data. Frontiers in Computer Science, 2. https://doi.org/10.3389/fcomp.2020.00031
Segovia, M., & Garcia-Alfaro, J. (2022). Design, modeling, and implementation of digital twins. Sensors, 22(14), 5396. https://doi.org/10.3390/s22145396
Shahzad, M., Shafiq, M. T., Douglas, D., & Kassem, M. (2022). Digital twins in built environments: An investigation of the characteristics, applications, and challenges. Buildings, 12(2), 120. https://doi.org/10.3390/buildings12020120
Vrabič, R., Erkoyuncu, J. A., Farsi, M., & Ariansyah, D. (2021). An intelligent agent-based architecture for resilient digital twins in manufacturing. CIRP Annals, 70(1), 349–352. https://doi.org/10.1016/j.cirp.2021.04.049
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Well Testing Journal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This license requires that re-users give credit to the creator. It allows re-users to distribute, remix, adapt, and build upon the material in any medium or format, for noncommercial purposes only.
