Small language models for agents

Small language models are being explored as cheaper specialized components inside agent systems, especially for routine or narrow subtasks. Why it matters: Routing every step to a frontier model can make agent workflows slower and more expensive than necessary. Smaller models may become useful workers for classification, extraction, formatting, and simple tool decisions.

Try this: Identify one low-risk subtask in an agent pipeline and test whether a smaller model can perform it reliably with fixed examples and an eval set.