An Autonomous Agentic Framework for Deep Inverse Photonic Design

We develop and demonstrate a framework for the inverse design of photonic metamaterials. When queried with a desired optical spectrum, the Agent autonomously proposes and develops a forward deep learning model, utilizes memory, and generates a final design. Our results suggest that autonomous agents have the potential to accelerate research in photonics and broader domains.