An agentic artificially intelligent X-ray scientist

Abstract Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision, remaining a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. We present the first demonstration of an AI agent which plans and executes experimental tasks, analyzes results, and iterates to achieve a scientific goal. Based on existing large language models and enhanced with the model context protocol, our AI agent was guided and tested using an in-house built virtual experimental setup which mirrors those which exist at large-scale X-ray scattering facilities, specifically here a six-circle diffractometer. It successfully transferred the knowledge to a real beamline and handled an experiment at a synchrotron X-ray source, where it correctly identified reference reflections and determined the orientation matrix---an essential first step in any type of single crystal scattering experiment. Our AI agent responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and showing readiness for addressing practical experimental situations. Our study provides a significant step toward autonomous operation across diverse experimental environments.