Alright, let’s talk about the AI landscape. We’ve all gotten pretty familiar with Large Language Models (think ChatGPT, Gemini, Llama, and the like). They’re genuinely impressive at generating human-like text, summarizing information, answering questions based on the data they were trained on, and even spitting out functional code. You can use a base model, pretrained on a massive chunk of the internet, or you can fine-tune one on your specific dataset, maybe your company’s internal docs, to make it an expert in a particular niche.
But there’s another term making the rounds now: agentic AI. Is this just the latest buzzword, some marketing spin, or is there a fundamental technical difference between an agentic system and a standard LLM, even one that’s been fine-tuned?
Spoiler alert: There’s a real difference, and it comes down to how they operate and what they can actually do.
AI / Fine-Tuned: Text Generation
Think about your typical LLM, whether it’s off-the-shelf or one you’ve specifically fine-tuned. Its core competency boils down to predicting the next piece of text.
During training, it learns an incredible amount about language patterns, grammar, facts (up to its knowledge cutoff), and even different reasoning styles, all derived from the massive dataset it took in. If you fine-tune it you’re essentially providing focused training examples, maybe question-answer pairs related to your product, to make it perform better within that specific domain. It adapts its internal weights based on this data.
When you give it a prompt, its operation is fundamentally about using these learned patterns (either the broad ones from pretraining or the specialized ones from fine-tuning) to calculate the most probable sequence of words or, more accurately, tokens (as I discussed in a previous post), that should follow your input.
The key takeaway here is that it’s reactive. It takes your text input and generates a text output. It doesn’t inherently create multi-step plans, interact with external systems autonomously, or pursue a broader goal beyond completing the current text generation task. While incredibly powerful for language-based tasks, it’s essentially confined to generating text based on its internal, static knowledge.
Agentic AI: AI that can do stuff!
Agentic AI represents a different architectural approach. It often uses an LLM as its core “brain” or reasoning engine, but it builds a system around that LLM, enabling it to pursue specific goals. Instead of just generating text describing how to do something, an AI agent attempts to actually accomplish the task.
This usually involves integratng several key capabilities that work together.
First, there’s planning. You give the agent a high-level objective, maybe something like “Research the current market trends for electric vehicles and generate a summary report.” The agent needs to break this down into a sequence of actionable steps. This might involve determining what specific information is required, identifying potential sources for that information, figuring out how to access those sources, and planning how to process the retrieved data.
A major differentiator is tool use. Agents are designed to interact with external resources beyond the LLM itself. These tools could be web search engines for fetching up-to-date information, code interpreters for running calculations or data analysis scripts, databases for querying structured data, or various APIs for interacting with other software systems (like looking up a card to add to your deck,). The ability to leverage external tools allows the agent to gather current information and perform actions in the real world, overcoming the limitations of the LLM’s static training data.
To effectively execute a plan involving multiple steps and tool interactions, an agent requires memory. This isn’t just about the LLLM’s context window. It can involve short-term memory, like a scratchpad, to keep track of the current plan, intermediate results, and the history of actions taken within a single task execution. It might also include long-term memory capabilities, allowing the agent to store and recall information from past interactions, learn successful procedures, or access relevant user preferences to improve its performance over time.
Finally, some more advanced agentic systems incorporate reflection or self-correction. This means the agent can monitor its own progress towards the goal. If a particular step fails (e.g., a web search returns irrelevant results, or an API call errors out), the agent migt analyze the reason for the failure, adjust its plan accordingly (perhaps by refining the search query or trying a different tool), and attempt the step again or pursue an alternative path.
The big difference
So, what’s the fundamental difference in a nutshell?
A standard or fine-tuned LLM primarily focuses on processing input text and generating output text based on its learned statistical patterns. It’s essentially a sophisticated text generator.
An agentic AI system, on the other hand, is built to achieve a specified goal by autonomously planning and executing a sequence of actions, often leveraging an LLM for reasoning and using external tools to interact with its environment.
Fine-tuning an LLM makes it better at generating text within a particular domain. Building an agent framework around an LLM empowers it to take actions within a specific environment to accomplish tasks.
Consider the task of finding flight options. You could ask a standard LLM: “What are some good websites to find flights from New York to London?” It will generate text listing relevant websites or perhaps describe the steps involved. You could instruct an AI agent: “Find me three flight options from New York to London departing next Tuesday, economy class, under $800.” The agent would plan, use tools, manage memory, potentially self-correct (if initial searches fail), and ultimately present you with concrete flight options, maybe even initiating the booking process if designed to do so.
Wrapping Up
Agentic AI isn’t necessarily a fundamentally new kind of underlying AI model, but rather a system architecture that harnesses the power of existing models (like LLMs) by adding layers for planning, memory, and tool interaction. This combination shifts from AI as a passive text generator to AI as an active, autonomous problem-solver capable of tackling complex tasks.