Agentic Digital Twins: Self-Evolving Models for Autonomous Systems

Abstract

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.