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AI驱动硬件设计的兴趣，这提出了一个问题：在代理式时代，高层次综合（HLS）是否仍然重要？我们认为HLS仍然至关重要。尽管我们预期成熟的代理式硬件系统将同时利用HLS和RTL，但本文聚焦于HLS及其在实现代理式优化中的作用。HLS提供的快速迭代周期、可移植性和设计可置换性，使其成为代理式优化的天然层级。本立场论文作出三点贡献。首先，我们阐释了为何HLS可作为代理式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——性能反馈不足、接口僵化以及可调试性有限——而这些正是AI代理具备独特优势可解决的问题。第三，我们提出了代理式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向AI代理转移。