Existing EDA environments have been predominantly organized around human interpretation, intervention, and control. Consequently, large language model (LLM)-based methods have thus far shown their greatest practical utility as copilots layered atop established tools and design flows. As chip design becomes increasingly heterogeneous, tool-intensive, and optimization-driven, however, there is growing interest in AI systems that can coordinate tasks across abstraction levels and operate in a closed loop with simulation, verification, and optimization. This shift is not merely a matter of incremental automation; rather, it raises a more fundamental question of how design intent, tool state, and optimization objectives should be represented, propagated, and acted upon across the EDA stack. Agentic AI is emerging as a consequential direction for EDA, pointing toward systems that can plan, orchestrate tools, and iteratively refine solutions across complex design workflows. This special session brings together experts from academia and industry to examine the opportunities and challenges of Agentic EDA across the design stack. Topics of interest include agent architectures, tool integration, human-agent collaboration, and related system and workflow considerations. More broadly, the session aims to interrogate how EDA systems and workflows may need to evolve to support increasingly capable AI agents, and what new forms of representation, interface, feedback, and evaluation may be required across the design flow. Agentic EDA is not only a new application of AI to existing tools, but also a lens through which to reconsider the foundations of future design automation, pointing toward next-generation AI-native systems for EDA.
