Small Language Models are the Future of Agentic AI

This paper presents a strong **position statement** arguing that **Small Language Models (SLMs)** are the **future of agentic AI**, despite the current dominance of **Large Language Models (LLMs)**. The authors contend that SLMs are **sufficiently powerful**, **more economical**, and **operationally more suitable** for the specialized and repetitive tasks common in AI agents. They provide **arguments grounded in modern SLM capabilities** and **inference efficiency**, advocating for a shift to **SLM-first architectures** or **heterogeneous systems** that use LLMs only when necessary. Furthermore, the paper outlines a **conversion algorithm** to help developers migrate existing LLM-based agents to more efficient SLM solutions and discusses **barriers to adoption** such as industry inertia and infrastructure investment.