Are small language models the future of agentic AI when efficiency and budget matter?

In recent months, a new discussion has gained momentum in the AI community: whether large language models are always the right foundation for AI agents. There is a discussion about the assumption that ever larger models are the natural choice for agentic systems. For decision-makers, this is not a technical detail but a strategic question about cost, reliability, and long-term scalability.

What's happening

AI agents are moving from experimentation into real operational use. Instead of a single chatbot answering questions, organizations increasingly deploy agents that plan, decide, call tools, and execute tasks in the background. These agents rely on language models as their reasoning and control layer.

At the same time, researchers are challenging the dominance of large language models in these systems. For example, a position paper from NVIDIA argues that many agentic tasks are narrow, repetitive, and predictable. For such tasks, smaller language models may already be sufficient and, in many cases, better suited.

What are AI agents

From a leadership perspective, an AI agent is not a human-like assistant that chats freely. It is better understood as a software component with a clear role. An agent observes a situation, decides what to do next, and takes action, often by calling APIs, searching internal systems, or triggering workflows.

Crucially, agents usually expose only a very narrow slice of language model functionality. They do not need creativity or rich conversation most of the time. Instead, they need consistency, speed, and predictable behavior. For example, an agent that classifies incoming support tickets, checks compliance rules, or schedules follow-up actions repeats the same patterns thousands of times.

This is where the choice of model becomes an architectural decision rather than a branding one.

Why small language models deserve attention

The paper's core claim is that the use of large language models in agent design is excessive and often misaligned with real needs. Large models excel at open-ended dialogue and general knowledge, but most agentic subtasks are scoped and non-conversational.

Small language models offer several practical advantages in this context. They run with lower latency, require less memory, and consume significantly fewer computational resources. This directly translates into lower operational costs and easier deployment, including on-device or on-premise scenarios where data governance matters.

Another often overlooked point is alignment. Agentic interactions require close behavioral alignment because agents act on behalf of the organization. Smaller models, trained or fine-tuned for a specific task, are often easier to control and audit than very large, general-purpose models.

Why this matters for executives

This debate is not about replacing one model with another. It is about efficiency, risk, and strategic optionality. If every agent invocation relies on a large cloud-hosted model, costs scale linearly with usage, and latency becomes a structural constraint.

Even a partial shift from large to small models can have a meaningful economic impact. Even replacing only some agent subtasks with small language models already lowers total cost of ownership. This is particularly relevant as agentic systems move from pilots to production and from dozens to millions of executions per month.

There is also a governance angle. Smaller models can be deployed closer to the data, sometimes even on user devices. This reduces data exposure and dependency on external providers, which is increasingly important in regulated European environments.

How this impacts you

Whether you lead within an enterprise, an SME, or an educational institution, agentic AI will increasingly operate in the background of your systems. The key question is not whether you use large or small models, but whether your architecture matches your actual use cases.

In practice, this often leads to heterogeneous agentic systems. General-purpose models remain valuable where language understanding and conversation are central. Small language models take over repetitive, well-defined tasks. This combination allows organizations to balance performance, cost, and control.

What to do next

Start by mapping your agentic use cases. Identify which tasks truly require broad language understanding and which are narrow and repetitive. This exercise alone often reveals significant optimization potential.

Next, challenge the assumption that one model fits all. Ask your teams or partners whether parts of your agent workflows could be handled by smaller, task-specific models without sacrificing quality.

Finally, treat model choice as a strategic decision, not a technical footnote. Cost structures, latency, data governance, and alignment all matter at scale. The discussion is a useful catalyst to rethink how AI resources are used responsibly and efficiently.

If this topic is relevant for your organization, feel free to reach out.