Invited: Agentic AI for Physical Design R&D: Status and Prospects

Recent advances in large language models (LLMs) and tool-using autonomous agents present new opportunities for accelerating research and development in physical design. Unlike earlier uses of machine learning that focused narrowly on prediction or optimization subroutines, agentic AI systems can comprehend user specifications, modify code, run EDA tools, analyze results, perform multi-step reasoning, and iteratively refine design heuristics. This paper surveys the emerging landscape of agentic AI for physical design R&D, with emphasis on (i) tool-integrated agents for algorithm evolution, debugging, and workflow automation, (ii) autonomous exploration of heuristic spaces in placement, routing, and partitioning, and (iii) interfaces between agents and traditional EDA frameworks. We analyze recent experience with multi-agent workflows and benchmark evaluation, highlighting current capabilities, limitations, and research frontiers. We conclude by articulating the long-term prospects of agentic AI as a catalyst for accelerated innovation in physical design, including autonomous algorithm discovery, continuous tool improvement, and closed-loop learning from large design corpora.