Imagine an AI assistant that could manage the complexities of a power grid, running simulations and optimizing performance with unprecedented speed and efficiency. That future might be closer than you think. Researchers have developed a groundbreaking multi-agent framework that empowers Large Language Models (LLMs) to handle the intricate task of power system simulations. Historically, LLMs have struggled with this type of challenge due to their limited domain-specific knowledge, rigid reasoning abilities, and difficulties in accurately managing the many parameters involved in power system simulations. This new research tackles these hurdles head-on. The key innovation lies in a feedback-driven multi-agent system. The system incorporates an enhanced retrieval-augmented generation (RAG) module to access and process relevant information effectively. It also features an improved reasoning module that allows the LLM to understand the complex logic of power system operations. Finally, a dynamic environmental acting module provides feedback, allowing the AI to learn from its mistakes and refine its simulations. Tested on both familiar and unfamiliar simulation tools (MATPOWER and DALINE), this framework achieved remarkably high success rates exceeding 93%. What’s more, each simulation took only about 30 seconds at a negligible cost, showcasing the potential for massive scalability. This research isn't just about making simulations faster and cheaper; it's about fundamentally changing how we interact with complex systems. Imagine using natural language to instruct an AI to design and execute power grid simulations, freeing up human researchers to focus on higher-level analysis and decision-making. While challenges remain, such as developing fully automated evaluation methods and extending the framework to manage multiple simulation tools concurrently, this research opens exciting new avenues for AI-driven research and development in power systems and beyond.