Agentic AI for Large-Scale Digital Twin Ecosystem Management

Abstract

The current paper discusses the use of agentic AI in large-scale digital twin ecosystem management where smart city infrastructures and energy grids are in discussion. The paper discusses multi-agent coordination issues, emergence of behaviors and safety issues in these interdependent systems. A simulation-based solution to this is the creation of autonomous agents that will maximize relationships between digital twins and make dynamic changes and effective distribution of resources. The study provides new agent-based models that can be useful in increased scalability and fault tolerance of the real world. Among the important results, it is possible to note that implementation of agentic AI promises the results of substantially enhancing responsiveness of the system and lowering energy intensity and providing more robust and resilient mobility networks in the urban environment. Moreover, this paper explains that decentralized coordination of agents results in more flexible and efficient complex infrastructural system management. These results can serve as a significant lesson when it comes to the future enhancement of the digital infrastructure optimization and present guidance to policymakers and urban planners intending to implement wiser, more sustainable landscapes on the grand scale.