The Evolving Role of LLMs in Agentic AI

Talking about agentic AI without getting excited about large language models (LLMs) feels naturally incomplete. LLMs provide such a powerful support to agentic systems that thinking of building robust agentic AI applications without the use of LLMs looks like cooking a recipe without a key ingredient. To understand the role of LLMs in agentic AI applications, let's first decode what LLMs are and their capabilities.

In "LLM" ,

L = Large scale of parameters and a massive amount of training data,

L = Language means it understands and generates human-like language and

M = Model represents machine learning/deep learning model.

Combining these two Ls and one M gives LLM, which are advanced language models, using very large parameters and massive amounts of data to pre-train machine /deep learning models to understand and generate human-like text. These are pre-train machine learning models because LLMs are already trained on data from the web and built on a machine learning model based on the neural network called the transformer model. To little bit dig further to understand under the hood architecture, transformer model uses 'attention mechanism'. Attention mechanism, as the name suggests, is a machine learning technique that focuses on the most relevant part of the input. It achieves this by assigning proper weight to each word/character of the input data.

Now, without delving too deep into the technical architecture of large language models (LLMs), let's focus back on agentic AI systems and the role LLMs play in them. A key characteristic of an agentic system is its ability to act independently with no or minimum human interaction. These are intelligent systems which can plan, decide, learn and adjust, pretty much mimicking human behavior of doing any task.

LLM plays a dynamic role in developing these intelligent systems, often called 'Agentic AI' systems. Let’s understand one by one how.

• Most people are familiar with LLMs in the context of generative AI. These models can understand, respond to and generate human-like text. LLMs (such as OpenAI's GPT series) have remarkable proficiency in processing natural language. This ability allows LLMs to engage in complex conversations, generate coherent narratives, and even provide insightful analyses on various topics. As a result, LLM serves as a valuable tool for agentic systems to interact human-like and interpret in a human-understandable way.

• Advanced LLMs have significantly improved their ability to support planning and reasoning tasks. LLMs utilize chain of thought mechanisms to break big tasks into small steps. LLM performs and refines reasoning using reinforcement techniques. This provides cognitive functionalities, such as planning & reasoning to the agentic systems.

• Next is decision-making ability; LLMs can provide necessary information to agentic AI systems to make informed decisions. They can link raw data with actionable insights to make well-informed decisions based on extensive analysis and contextual understanding. This synergy not only enhances the efficiency of the decision-making processes of an agentic system but also increases the likelihood of achieving desired outcomes.

• Action autonomous tasks; LLM can break tasks into multiple small-tasks and can write code for calling apis or other scripts. For example, when tasked with retrieving data from a web service, an LLM can outline the steps required to complete the task, write the necessary code snippets and facilitate API calls. Also, handle responses and manage error scenarios. This gives agentic AI systems the capability to action autonomous tasks.

In a nutshell, LLMs give agentic AI system the ability to act independently and make choices based on their programming and learned experiences. LLMs play a dynamic role in developing robust agentic AI system by helping interpret context, respond appropriately to a wide range of inputs and take actions. These capabilities are crucial for developing agentic AI systems that can engage in tasks requiring understanding and adaptability that mimics human reasoning.