AI agents promise to transform scientific discovery, and much of their potential comes from the ability to use existing tools for computational modeling and lab robotics. We explore how agents can autonomously generate hypotheses, computationally validate ideas, build reproducible experimental protocols, and control robotic platforms to execute real-world experiments. This session outlines the core infrastructure required to close the loop between in-silico reasoning and physical experimentation.