AI Agents VS LLMs: What’s the Difference?

As AI becomes more and more mainstream, it’s common to get overwhelmed with all the new terms emerging. That problem is what’s brought us to this article: AI Agents VS LLMs.

Despite their differences, many people consider them the same. In this article we’ll dive deeper into both the terms separately, then briefly discuss the differences between them.

What are AI Agents?

AI agents are software systems that can self-direct tasks and execute them on their own. That’s why they are often described as autonomous AI agents as well.

A human user would set a goal for an AI agent to achieve e.g. clean up a dataset. The AI agent would then use its capabilities to independently decide how to best achieve that goal.

These capabilities include processing multimodal information like text, voice, video, audio and code. They can even reason, have conversations, think about problems, answer questions, look up information, and make decisions – all without any human intervention.

Over time these models have the capacity to improve themselves, adjusting to newer conditions and contexts.

What Does an AI Agent Do?

We’ve established that an AI agent can independently handle tasks, but what does that exactly look like?

There are a few key features that AI agents have that enable them to perform their duties. These are:

Reasoning

Acting

Observing

Planning

Collaborating

Self-Refining

Let’s discuss them one by one.

Reasoning

When an AI agent ‘reasons’, it refers to using the built-in logic of the software to identify problems, form conclusions and solve problems. Since AI doesn’t have a human brain, it performs this process through large sets of available data, which it is trained on.

An advanced AI agent with powerful reasoning capabilities will be able to analyze data, find patterns, and make well-informed decisions based on the context.

Acting

Acting on its own plans is one of the main things that differentiates AI agents from other types of AI bots. An AI agent can act independently rather than relying on pre-defined rules like scraper bots.

This could involve both physical acting, or digital interaction. With physical actions, an AI agent alone cannot perform tasks, but has to be integrated with robotics. But for digital interactions, such as carrying out transactions, making changes to a document or placing orders.

Observing

A big advantage of using AI agents is that it understands context. It understands context by observing an environment or situation and gathering relevant information about it, before responding to, or deciding on the best course of action.

To carry out this feature, it often relies on external APIs, computer vision, natural language processing (NLP), and/or deep learning. Through these tools, it is possible for the AI agent to perceive objects, text, and other things to develop the context it needs to react appropriately.

Planning

Due to its significant ability to gather, process, and analyze large amounts of data, it is able to then plan its actions. Every goal needs a strategic plan to execute its course of action effectively.

To develop its plan, the AI agent will identify steps and potential actions to take in order to complete its goal. It would also take into account future expectations and any hiccups or bottlenecks that may present itself to plan for them beforehand.

Collaborating

Another key feature of AI agents is their ability to collaborate — not just with humans, but with other agents or external systems. This is often referred to as multi-agent collaboration. In practice, this could mean multiple AI agents dividing a complex task into smaller parts and then working together to complete them.

For example, one agent might focus on retrieving data, while another interprets it, and a third generates the final report. This collaborative ability allows agents to operate as teams, scaling productivity in ways that a single model working alone cannot.

Self-Refining

A defining trait of autonomous AI agents is self-refinement. Over time, they can learn from successes and mistakes, fine-tuning their strategies to perform tasks more effectively. This doesn’t mean they completely retrain themselves like a new large language model, but rather that they adapt their actions based on feedback, results, or environmental changes.

For instance, if an AI agent frequently makes scheduling errors when booking meetings, it can refine its approach by integrating better context handling or by adjusting how it interprets user preferences. This self-improvement loop makes them increasingly reliable over time.

What is an LLM?

A Large Language Model (LLM) is an advanced type of AI model designed to understand and generate human-like text. Examples include ChatGPT, Claude, and Gemini. Unlike agents, LLMs are not autonomous. They operate primarily as predictive text generators: given an input (prompt), they predict and produce the most likely next word or sequence.

LLMs are the backbone of many AI systems because they excel at language-related tasks such as answering questions, summarizing, translating, coding, and even generating creative content. However, on their own, they do not take action — they only produce text outputs.

How Do LLMs Work?

At the core, LLMs rely on a transformer architecture, which allows them to process vast amounts of training data. By learning statistical relationships between words, phrases, and contexts, they become capable of producing coherent and contextually appropriate responses.

Key features of how LLMs work include:

Training on massive datasets – billions of parameters are adjusted during training to capture patterns of human language.

Attention mechanisms – transformers use attention to weigh the importance of different words in context, helping models better understand nuance.

Prompt-dependency – LLMs only act when prompted; they don’t plan or execute actions independently.

Fine-tuning & RAG – models can be customized through fine-tuning or enhanced with retrieval-augmented generation (RAG), which pulls in external knowledge sources.

AI Agents VS LLMs: Similarities & Differences

Although people often confuse AI agents with large language models (LLMs), the two concepts are not the same. They share common ground in some areas but diverge significantly in how they operate, what they’re capable of, and the role they play in real-world applications.

Similarities between AI Agents and LLMs

Language Understanding

Both LLMs and AI agents rely heavily on natural language processing. This means they can understand human input and respond in a way that feels conversational. For instance, when you type a question into ChatGPT or another model, the LLM interprets the text and produces a coherent reply. Similarly, an AI agent also uses language understanding to receive instructions, break them down into goals, and act on them. In other words, without strong language comprehension, neither LLMs nor agents would function effectively.

Machine Learning Foundations

Another key similarity is that both technologies are powered by machine learning and deep learning. LLMs are trained on enormous datasets of text to capture language patterns, while AI agents often use these same models as a base for reasoning and decision-making. This shared foundation is what makes both systems capable of handling complex, context-rich interactions.

Human-Centered Interaction

At their core, both autonomous AI agents and LLMs are built to make technology more accessible to humans. Instead of requiring users to code or issue highly technical commands, they allow people to communicate in natural language. This similarity is especially important for businesses and everyday users, because it lowers the barrier to using advanced AI tools.

Differences between AI Agents and LLMs

LLMs as the Foundation

Large Language Models (LLMs) form the foundation of modern AI applications. They provide the core intelligence: the ability to process natural language, reason over information, and generate useful responses. On their own, LLMs are reactive. They respond when prompted, but they do not plan, act, or remember beyond the immediate interaction.

AI Agents as an Extension

AI agents are built on top of LLMs. They take the raw capabilities of the model and add the ability to set goals, plan steps, and interact with external systems. For example, while an LLM can write an email draft, an AI agent can write the draft, send it, create a reminder, and adapt its tone to your preferences. In this sense, the LLM acts as the brain, while the agent provides the tools and structure needed to act in the world.

Expanding the Scope

LLMs are primarily focused on language tasks such as text generation, summarization, or analysis. When combined with agent frameworks, this scope expands into broader digital and even physical actions. Agents can query databases, update spreadsheets, interact with APIs, or control robotics. They can also collaborate with other agents, dividing tasks to complete complex workflows more efficiently.

Human-Centered Value

The most important outcome of this relationship is accessibility. LLMs lower the barrier by allowing people to interact with AI in natural language. AI agents then extend that benefit by turning language into action, making it easier for individuals and organizations to automate processes, manage tasks, and integrate AI into everyday life.

Conclusion

While Large Language Models serve as the linguistic engine, AI Agents add the missing layers of autonomy, planning, and action-taking. Think of an LLM as a highly intelligent conversationalist, and an AI Agent as a full-fledged problem-solver that uses that conversational ability to achieve real-world goals.

As we move deeper into the agentic AI era, the distinction between LLMs and AI agents becomes crucial: one provides the intelligence, the other delivers the initiative. Together, they represent the future of human-AI collaboration.