Small language models (SLMs) can handle specialized, repetitive tasks in agentic AI as effectively as large models.
SLMs are more cost-efficient and suitable for many agent deployments due to lower computational requirements.
For tasks requiring general conversational abilities, combining multiple different models in heterogeneous agentic systems is recommended.
The authors outline an algorithm to convert large language model–based agents into small language model–based agents.
Adoption barriers remain, but even a partial shift to SLMs could significantly reduce operational and economic costs in the AI agent industry.
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