Imagine an AI assistant that can not only understand complex fire dynamics simulations but also run them on a supercomputer. Researchers are exploring this exciting frontier with FoamPilot, an experimental AI agent designed to simplify the intricate world of FireFOAM, a powerful fire simulation software. FireFOAM, built on the open-source CFD toolbox OpenFOAM, allows scientists to model everything from a small flame to a large-scale warehouse fire. However, configuring and running these simulations requires significant expertise in fire science, fluid dynamics, and high-performance computing. FoamPilot aims to bridge this gap by providing three core functions: Code Insight, Case Configuration, and Job Execution. Code Insight allows users to quickly find and interpret relevant sections of the FireFOAM source code, replacing tedious manual searches with a smart, AI-powered lookup. Case Configuration takes natural language instructions like "make the burner twice as big" and automatically modifies the simulation setup accordingly. Perhaps the most ambitious feature is Job Execution, where FoamPilot manages the submission and execution of simulations on high-performance computing clusters. This could dramatically democratize access to large-scale simulations, allowing researchers without HPC expertise to leverage powerful computing resources. While still in its early stages, FoamPilot shows promising results, particularly for simpler tasks. It successfully interprets and modifies simulation parameters and can even run basic simulations. However, more complex scenarios, like multi-step modifications or intricate HPC job submissions, still present challenges. The research highlights the potential of AI agents to revolutionize scientific simulations. By handling tedious setup and execution tasks, these agents can free up scientists to focus on the science itself. Imagine a future where researchers can simply describe their desired simulation in plain English, and the AI handles the rest. While not there yet, FoamPilot provides a glimpse into this exciting future. Challenges remain, such as improving the agent's ability to handle complex, multi-step tasks and ensuring its reliability in managing powerful computing resources. Further development and integration with emerging AI technologies like multimodal learning (allowing the agent to “see” the simulation geometry) could unlock even greater potential. As AI models continue to evolve, we can expect to see even more sophisticated agents like FoamPilot emerge, accelerating scientific discovery and making complex simulations accessible to a wider range of researchers.