Digital twins, virtual replicas of physical systems, offer incredible potential for optimizing everything from manufacturing processes to entire cities. But harnessing their full power often requires expert human intervention to interpret complex data and make informed decisions. Imagine if we could automate that process. New research explores how AI agents powered by large language models (LLMs) can take on this role, autonomously running simulations and finding the best parameters to achieve specific goals. The researchers designed a multi-agent system where different LLMs specialize in observing, reasoning, deciding, and summarizing. Like a team of expert consultants, these AI agents work together, analyzing data from the digital twin and iteratively adjusting parameters to optimize the simulated process. Think of it like a self-tuning engine for your digital twin. In a case study involving a simulated mixing process, the AI agents successfully controlled the order and timing of adding different materials to achieve a homogenous mixture. This approach not only automates complex tasks but also makes digital twin technology more accessible to non-experts. While still in its early stages, this research points to a future where AI-powered digital twins can autonomously optimize complex systems, unlocking new levels of efficiency and innovation across various industries. Challenges remain in refining the system and applying it to more complex simulations, but the potential is vast. As AI continues to evolve, we can expect even more sophisticated digital twin applications that push the boundaries of what's possible in simulation and control.