Small Language Models are the Future of Agentic AI
Authors: name: Peter Belcak, name: Greg Heinrich, name: Shizhe Diao, name: Yonggan Fu, name: Xin Dong, name: Saurav Muralidharan, name: Yingyan Celine Lin, name: Pavlo Molchanov

Large language models (LLMs) are often praised for exhibiting near-human
performance on a wide range of tasks and valued for their ability to hold a
general conversation. The rise of agentic AI systems is, however, ushering in a
mass of applications in which language models perform a small number of
specialized tasks repetitively and with little variation.
  Here we lay out the position that small language models (SLMs) are
sufficiently powerful, inherently more suitable, and necessarily more
economical for many invocations in agentic systems, and are therefore the
future of agentic AI. Our argumentation is grounded in the current level of
capabilities exhibited by SLMs, the common architectures of agentic systems,
and the economy of LM deployment. We further argue that in situations where
general-purpose conversational abilities are essential, heterogeneous agentic
systems (i.e., agents invoking multiple different models) are the natural
choice. We discuss the potential barriers for the adoption of SLMs in agentic
systems and outline a general LLM-to-SLM agent conversion algorithm.
  Our position, formulated as a value statement, highlights the significance of
the operational and economic impact even a partial shift from LLMs to SLMs is
to have on the AI agent industry. We aim to stimulate the discussion on the
effective use of AI resources and hope to advance the efforts to lower the
costs of AI of the present day. Calling for both contributions to and critique
of our position, we commit to publishing all such correspondence at
https://research.nvidia.com/labs/lpr/slm-agents.