Empowering Digital Twins With Agentic AI: Applications, Case Studies, and Limitations

Digital Twins (DTs) are emerging as a promising paradigm because of their ability to track real-time data from the physical entity they mirror while also providing simulation capabilities. Agentic AI (AAI), a rapidly evolving branch of AI, is becoming a standard paradigm for its ability to make autonomous decisions in a dynamic environment with goal-oriented behavior. This paper explores the integration of Agentic AI in Digital Twins, resulting in autonomous, intelligent, and adaptable digital twins, and examines how Agentic AI-augmented digital twins enable predictive analytics, dynamic decision-making, and proactive system control in various sectors such as healthcare, manufacturing, finance, smart cities, and supply chains. The paper also explores a case study where this technology can be used to increase operational efficiency and support strategic planning using simulation and data analysis methodologies. This work highlights how the integrated technology is already being used in various industries, potential applications in other domains, and the challenges ahead for the integration.