Small language models (SLMs) are argued to be more suitable and economical than large language models (LLMs) for agentic AI systems that focus on specialized tasks. The authors propose that a shift towards SLMs will significantly impact the AI agent industry and suggest a conversion algorithm from LLMs to SLMs, while also addressing potential adoption barriers. They invite contributions and critiques to foster discussion on optimizing AI resources and reducing costs.