Optimizing Selection for Agentic AI Language Models

Choosing a Language Model for Agentic AI

Discussions on agentic AI and generative AI frequently bring a concept known as a language model.

Before building an agentic AI system, a good understanding of what language models are, what they do, and their role in an agentic AI system can help decide the exemplary architecture for agentic AI applications by choosing the right language model.

Let's dive straight into Language models. In the human world, languages are the method to communicate with another human who understands, interprets, predicts incomplete sentences, and responds in the same language. Similarly, in artificial intelligence, language models are machine learning models designed and trained to learn, understand, and predict the likelihood of words in natural language to make a meaningful sentence for machines to communicate and respond in human-like language.

And just like humans, when a child or someone is learning a language, they have limited knowledge and ability to understand and interact compared to an adult who became an expert in language after learning, reading, and gaining knowledge from an ample amount of resources, machine language model types are based on the amount of data these models are trained on. These model types are classified as small, medium, and large. Smaller models are smaller, simpler, and more efficient on domain-specific tasks, while large language models are general-purpose, more complex, and resource-intensive models. Medium models fall in between the category of these two, which offer the accuracy and performance of large language models and the efficiency of small language models. Examples of large language models(LLM) are GPT, LangChain, Hugging Face, and Deepseek. Some examples of small language models(SLM) are Mistral 7B, Llama 3.2, and Phi 3.   Lamma 3.1 series and Mistral AI also offer some medium-scale language models.

These language models based on transformer architecture are utilized in Agentic AI systems where natural language processing is highly required. Selecting a language model depends on the agentic AI use case. Small language models are more suitable in agentic applications where efficiency, excellence in a limited area, cost-effectiveness, less computing power, real-time processing, and data required to process close to the source.  These are the most suitable in enterprise setups where privacy, security, and trust are highly expected. Business applications, enterprise task automation and integration for repetitive tasks, and document summarization are some examples where small language models are useful.

On the other hand, large language models are for complex agentic applications that require multi-step planning & reasoning and sophisticated agents capable of doing full-end-to-end human action. Considering factors such as the domain of application, the expected user interactions, and the types of data the system will process can guide the decision-making process in selecting the most suitable language model.

Furthermore, agentic AI systems do not always need to utilize language models. The question arises, is it possible to build an Agentic AI system without using any language models? The answer is yes; an agentic system can be built without a language model if the application does not require any natural language processing.

Finally, the role of language models in building agentic AI systems is significant. These models serve as the backbone of communication and interaction within these systems, powering them to understand, generate, and respond to human language. However, it is crucial to recognize that the specific needs of the intended use case should drive the selection of an appropriate language model. The goal should be for the system to operate efficiently and effectively by optimizing performance while minimizing unnecessary complexity or overhead. This strategic alignment fosters the development of more responsive and intelligent agentic AI systems and paves the way for innovative applications across enterprises.