| Abstract Scope |
Advanced scientific facilities,including self-driving laboratories, are revolutionizing discovery by automating repetitive tasks and enabling rapid experimentation. However,these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for ever more sophisticated instrumentation.This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models, as a transformative tool to achieve this goal. We present our approach to developing a pipeline for operating a robotic station dedicated to the design of novel materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex workflows, optimizing their performance through human input and iterative learning.We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities. |