The rise of large language models has sparked interest in AI-driven hardware design, raising the question: does high-level synthesis (HLS) still matter in the agentic era? We argue that HLS remains essential. While we expect mature agentic hardware systems to leverage both HLS and RTL, this paper focuses on HLS and its role in enabling agentic optimization. HLS offers faster iteration cycles, portability, and design permutability that make it a natural layer for agentic optimization. This position paper makes three contributions. First, we explain why HLS serves as a practical abstraction layer and a golden reference for agentic hardware design. Second, we identify key limitations of current HLS tools, namely inadequate performance feedback, rigid interfaces, and limited debuggability that agents are uniquely positioned to address. Third, we propose a taxonomy for the symbiotic evolution of agentic HLS, clarifying how responsibility shifts from human designers to AI agents as systems advance from copilots to autonomous design partners.

翻译：大型语言模型的兴起引发了人们对人工智能驱动硬件设计的兴趣，这提出了一个问题：在智能体时代，高层次综合（HLS）是否仍然重要？我们认为HLS依然不可或缺。尽管我们预期成熟的智能体硬件系统将同时利用HLS和RTL，但本文聚焦于HLS及其在实现智能体优化方面的作用。HLS凭借其更快的迭代周期、可移植性和设计可置换性，使其成为智能体优化的天然层次。本立场论文作出三点贡献。首先，我们阐释了为何HLS可作为智能体硬件设计的实用抽象层和黄金参考标准。其次，我们指出了当前HLS工具的关键局限性，即性能反馈不足、接口僵化以及可调试性有限，而这些正是智能体独具优势能够解决的问题。第三，我们提出了智能体HLS共生演化的分类框架，阐明了随着系统从辅助工具演进为自主设计伙伴，设计责任如何从人类设计师转移到AI智能体。