NVIDIA's recent research challenges the prevailing notion that larger language models are inherently superior for developing AI agent systems, advocating instead for the efficiency and adequacy of Small Language Models (SLMs) in handling most agent tasks. The study highlights that many agent functions, such as intent classification and data extraction, are narrow and repetitive, making them well-suited for SLMs, which offer sufficient capability, operational benefits, and significant cost savings. NVIDIA outlines a comprehensive five-step process for transitioning from large language models (LLMs) to SLMs, emphasizing data-driven decisions and efficient resource use. This approach not only reduces infrastructure costs but also democratizes access to advanced AI capabilities, promoting innovation and experimentation by lowering the financial barriers typically associated with large models. The research suggests that employing a mixed model architecture, where SLMs handle routine tasks and LLMs are reserved for complex queries, optimizes performance and cost-effectiveness.