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AI.

AI.

AI.

You've probably heard it a zillion times,

especially when people start talking about "AI agents".

But what does it actually mean?

Most explanations either go over your head with technical explanation or they dumb things down too much.

This article is for the rest of us:

the ones with no technical background, but who use AI tools and want to understand what's really going on.

We're going to walk through it step by step:

Let's Start with ChatGPT something you already use.

Then we'll move into AI workflows.

Finally we'll talk about what makes something a true AI agent.

And yes those fancy terms like RAG and ReAct are much simpler than they sound.

Level 1: Large Language Models (LLMs)

Let's start with the basics.

ChatGPT, Claude, Google Gemini, they're all built on large language models.

These models are great at generating and editing text.

They work like this:

You give a prompt (input).

The model gives you a response (output).

For example:

If you ask ChatGPT to draft a polite email asking for a coffee chat,

it'll generate something polished and professional.

It's using patterns it learned during training to craft that response.

But if you ask it something like,

"When's my next coffee chat?" …you already know it won't work.

That's because the model doesn't have access to your calendar.

That reveals two key traits of LLMs:

1. They don't have access to your private data.

Like your calendar or files,

unless you specifically give them access through a connected tool.

2. They're passive.

They sit there waiting for you to give a prompt.

Keep both of those points in mind,

they'll matter later.

Level 2: AI Workflows

Now let's add a bit more complexity.

What if I tell the LLM:

"Whenever I ask about a personal event, search my Google Calendar first before answering."

Now, when I ask

"When's my coffee chat with Elon Husky?"

The LLM could check my calendar and give me the right answer.

But what if I then ask,

"What's the weather like that day?"

It fails again.

Why?

Because we only told it to check the calendar not the weather.

That's the nature of a workflow.

It can perform specific steps, but only the ones we explicitly define.

This process is the step by step instructions and it is called control logic.

Let's say I expand the workflow:

• Check the calendar.
• Look up the weather.
• Use a text to audio tool to read the forecast aloud.

Even if I build a 100-step process like this,

it's still just a workflow.

Because I, the human made all the decisions about what happens and in what order.

Pro Tip:

The term RAG (Retrieval Augmented Generation) is just a fancy way of saying the AI is allowed to look something up" like fetching information from an external source before answering.

That's part of a workflow too.

Level 3: AI Agents

So what separates an agent from a workflow?

Let's look again at what I do manually:

1. I decide

what's the best way to structure this task?

2. I act

by connecting tools like Sheets, Perplexity, and Claude.

In a workflow, I'm the decision maker.

In an AI agent, the LLM becomes the decision maker.

That's the entire difference.

If the LLM is given a goal like "create and post daily news content"

it needs to reason through:

> "What's the best way to collect articles?

> "Should I compile full articles or just links?"

> "What tool should I use Google Sheets or something else?""

Then it needs to act:

> "Which model should I use to summarize?

> Do I need a second model to improve tone?

> Which one writes better for LinkedIn?"

Pro Tip:

Most AI agents use the ReAct framework. It stands for Reason + Act.

The model must think about the next step and carry it out.

A Third Trait: Iteration

Let's say the AI writes a LinkedIn post and it's not great.

In a workflow I would tweak the prompt and try again.

But an agent can evaluate its own output:

> "How can I improve this?

Let's rate it using LinkedIn engagement rules and try a new version."

The agent iterates until it meets its own quality bar.

That loop reasoning, acting, checking, improving,

that is what makes it different from a standard automation.

A Real AI Agent in Action

Andrew Ng built a demo that shows what an AI vision agent can do.

When you search for something like "skier," the agent:

1. Reasons "A skier is probably someone on skis going fast through snow."

2. Acts: it searches through video clips, labels the right footage and brings back a result.

Nobody manually labeled those clips.

No tags like "mountain" or "snow" were added by a human.

The agent figured it out.

The underlying system is complex.

But to the user, it just feels like magic.

Summary: Three Levels of AI

Let's wrap it all up:

Level 1: LLMs

You give a prompt.

The model gives a response.

No tools, no reasoning.

Level 2: Workflows

You give a prompt and define the steps.

The model follows them.

Tools may be used, but you're still in control.

Level 3: Agents

You give a goal.

The model reasons, chooses tools, takes action and improve its own output.

The LLM becomes the decision maker.

That's the leap from tool to teammate.

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