If you’ve been hearing the word “agent” tossed around alongside AI a lot lately, you’re not alone. The rise of terms like “AI agents,” “agentic workflows,” and “reasoning frameworks” has left many everyday AI users wondering: What exactly is the difference between regular AI and an AI agent?
This article breaks it down simply—no tech background required. If you’ve ever used tools like ChatGPT, Google Gemini, or Claude, you’re already familiar with AI at a basic level. Now, let’s explore how things evolve from regular chatbots into intelligent agents that can think, act, and even improve on their own.
Level 1: Large Language Models (LLMs)
Let’s start at the foundation—Large Language Models (LLMs). Tools like ChatGPT, Gemini, and Claude are all powered by LLMs. These models are excellent at generating and editing text based on what you type in.
Imagine this: you ask ChatGPT to write a polite email requesting a coffee chat. You provide the prompt (input), and the LLM responds with a well-worded email (output). Simple, right?
But now, let’s say you ask ChatGPT, “When is my next coffee chat?” This is where it falls short—because it doesn’t have access to your calendar. It can’t answer the question because it lacks personal or private data.
Here are two important takeaways about LLMs:
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They have limited access to private information (like your calendar or company data).
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They are passive—they wait for your prompt and then respond. That’s it.
Level 2: AI Workflows
Next, let’s move up a level to AI workflows. These are a bit more advanced.
Say you tell the AI: “Whenever I ask about a personal event, check my Google Calendar first.” Now, if you ask about your coffee chat with Elon Husky, the AI will fetch your calendar info and give you an accurate answer.
But there’s a catch—what if you then ask, “What’s the weather like that day?” The AI won’t know, because it wasn’t told to check the weather. The AI can only follow a path you defined.
This is the core trait of AI workflows:
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They follow strict, human-defined steps, also called control logic.
You can build longer workflows too. For instance, you could set the AI to:
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Access your calendar
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Check the weather
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Convert the result into speech using a text-to-audio model
Still, no matter how complex it gets, if you set the rules, it’s an AI workflow—not an AI agent.
By the way, you may have heard the term RAG (Retrieval Augmented Generation). In simple terms, it’s just a way for AI to look up external data before answering. For example, checking your calendar or browsing the web before responding. It’s still just part of a workflow.
Here’s a real-world example:
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You collect article links in Google Sheets.
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The AI summarizes them using Perplexity.
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Then Claude writes social media posts based on those summaries.
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You schedule everything to run daily at 8 a.m.
That’s a well-built AI workflow. But if you don’t like the social post Claude created, you still have to go back and rewrite the prompt. That trial and error? Still handled by a human.
Level 3: AI Agents
Now, here’s where it gets exciting—AI agents.
Let’s take that same workflow and tweak one big thing: Replace the human decision-maker (you) with an AI model.
To become an agent, the AI must do two things:
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Reason – Figure out how to solve a problem
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Act – Use tools to execute a plan
In our example:
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The AI decides that collecting article links in Google Sheets is better than copying and pasting everything into Word.
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It chooses to use Perplexity to summarize and Claude to write posts—without you telling it to.
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And if the LinkedIn post isn’t good enough? The AI adds a step to critique its own output, maybe using another AI model, and improves it until it’s satisfied.
This process of improving its own work over and over again is called iteration—and it’s something AI agents do automatically.
One popular setup for building agents is the ReAct framework (Reason + Act). It’s a simple yet powerful structure for AI agents to think through a task and then take action using tools.
Real-World AI Agent Example
Let’s look at a real example created by AI expert Andrew Ng. He built a demo where an AI agent is asked to find video clips of a “skier.”
Here’s how it works:
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The AI first reasons—“What does a skier look like?”
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Then it searches through videos to find relevant clips.
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It indexes the footage and returns it to the user—all without a human tagging anything manually.
This may sound subtle, but it’s a huge shift. Instead of someone spending hours tagging video clips, the AI handles the whole thing.
That’s the power of AI agents.
Wrapping It All Up
Let’s visualize the differences clearly:
| Level | Description | Who Makes Decisions? |
|---|---|---|
| LLM | You provide an input, AI gives output. | Human |
| Workflow | You program a step-by-step path. AI follows it. | Human |
| Agent | You give a goal. AI reasons, acts, and iterates. | AI |
The moment an AI goes from passively following instructions to actively deciding and improving—that’s when it becomes an agent.
So, whether you’re using AI to write emails, build marketing content, or automate research, understanding these three levels helps you see where the future is headed—and how you can take advantage of it.
