Agentic Digital Twins: Self-Evolving Models for Autonomous Systems

Abstract

The agentic digital twin has the potential to usher in a new era of fully autonomous systems that can learn, make decisions, and adapt in real-time. Autonomous interaction with the environment to maximize performance makes these models self-aware and learning, in contrast to conventional and immutable digital twins. Their uses in bespoke healthcare, industrial automation, and robotics will be covered in the study. Robotics: Agentic digital twins could let robots optimize productivity in real time and cut down on downtime. They use predictive maintenance to make industrial automation systems more reliable. When used to healthcare, they allow for real-time adjustments to treatment plans based on patient data, which improves results. With the help of case studies and performance metrics, this article lays out the pros and cons of using agentic digital twins. There is hope for the future of autonomous technology because the results show significant improvements in efficiency, adaptability, and scalability.