This paper explores whether small language models (SLMs), when integrated into sophisticated agentic AI frameworks, can achieve performance comparable to large language models (LLMs) for hardware design tasks. It demonstrates that strategic task decomposition and iterative refinement enable SLMs to offer significant efficiency and cost advantages without sacrificing quality, challenging the notion that bigger models are always better.