Understanding AI agents doesn’t have to be complicated. If you use Chatgpt, Google Gemini, or Claude regularly but feel confused by terms like “AI agents” and “RAG,” this guide is for you.
We’ll break down everything in three simple levels, using examples you’ll actually encounter in real life.
What Are AI Agents and Why Should You Care?
AI agents are smart computer programs that can think, plan, and take actions to complete tasks without constant human guidance. Unlike regular chatbots that just respond to your questions, AI agents can work independently to solve complex problems.
Think of it this way: ChatGPT is like a smart assistant who answers when you ask. An AI agent is like hiring a virtual employee who can figure out what needs to be done and do it.
Level 1: Understanding Large Language Models (LLMs)
Before we talk about AI agents, let’s start with what you already know.
What Are LLMs?
Popular AI chatbots like ChatGPT, Google Gemini, and Claude are built on Large Language Models (LLMs). These are excellent at reading and writing text.
Here’s how they work:
- You give them input (your question or request)
- The LLM produces output (their response) based on their training
Example in Action
If you ask ChatGPT to “draft an email requesting a coffee meeting,” it will write a polite email for you. Your request is the input, and the email is the output.
The Big Limitation
But what happens if you ask ChatGPT “When is my next coffee meeting?” It will fail because it doesn’t know your personal information or have access to your calendar.
This shows two key traits of LLMs:
- Limited knowledge: They don’t know your personal or company data
- They’re passive: They wait for your request, then respond
Level 2: AI Workflows – Following a Set Path
Now let’s add some power to our LLM example.
What Are AI Workflows?
Imagine telling an LLM: “Every time I ask about a personal event, first check my Google Calendar, then give me an answer.”
With this logic, when you ask “When is my coffee chat with my colleague?” the LLM will:
- Search your Google Calendar
- Find the information
- Give you the correct answer
The Problem with Workflows
But what if your next question is “What will the weather be like that day?” The LLM will fail because it’s programmed to only check your calendar, which doesn’t have weather information.
This is the key trait of AI workflows: they can only follow predefined paths set by humans.
What Is RAG?
You might have heard the term RAG (Retrieval Augmented Generation). Don’t let the fancy name scare you. RAG is simply when an AI looks things up before answering your question – like checking your calendar or a weather service.
RAG is just a type of AI workflow.
Real-World Example
Here’s a simple AI workflow using make.com:
- Step 1: Collect news article links in Google Sheets
- Step 2: Use Perplexity to summarize the articles
- Step 3: Use Claude to write LinkedIn and Instagram posts
- Step 4: Schedule this to run automatically every day at 8 AM
This is an AI workflow because it follows my predefined path. If I don’t like the output, I have to manually go back and fix the instructions.
Level 3: AI Agents – The Game Changer
Here’s where things get exciting.
What Makes an AI Agent Different?
In my workflow example above, I (the human) make all the decisions:
- Reasoning: I decide the best approach to create social media posts
- Action: I choose which tools to use (Google Sheets, Perplexity, Claude)
The one massive change that turns an AI workflow into an AI agent is replacing the human decision-maker with an LLM.
How AI Agents Think and Act
An AI agent must:
- Reason: “What’s the most efficient way to compile these news articles? Should I copy-paste each article? No, it’s better to compile links and use another tool to fetch the data.”
- Act: “Should I use Microsoft Word? No, Google Sheets is better since the user already connected their Google account.”
The REACT Framework
Most AI agents use the REACT framework:
- RE = Reason (think about the problem)
- ACT = Act (use tools to solve it)
Simple, right?
AI Agents Can Improve Themselves
Remember when I had to manually rewrite prompts to make my LinkedIn posts funnier? An AI agent can do this automatically.
It might think: “I’ve drafted a LinkedIn post. Let me add another step where I critique my own work based on LinkedIn best practices. I’ll repeat this until I meet all the criteria.”
Real-World AI Agent Example
Andrew Ng created a demo where you can search for “skier” in video footage. The AI vision agent:
- Reasons: “What does a skier look like? A person on skis, going fast in snow”
- Acts: Searches through video clips, identifies skiers, and returns the clips
No human had to watch all the footage and tag it manually. The AI agent did everything.
Quick Summary: The Three Levels
Level 1: LLMs
- You provide input
- LLM gives output
- Simple and direct
Level 2: AI Workflows
- You provide input
- LLM follows your predefined path
- May use external tools
- Key trait: Human programs the path
Level 3: AI Agents
- You provide a goal
- LLM reasons how to achieve it
- Takes action using tools
- Observes results and improves
- Produces final output
- Key trait: LLM is the decision-maker
Why This Matters for You
AI agents represent a massive shift from tools that respond to commands to tools that can think and work independently. As they become more common, they’ll change how we work, shop, learn, and solve problems.
Understanding these concepts now helps you:
- Make better decisions about AI tools
- Prepare for changes in your industry
- Take advantage of new opportunities
What’s Next?
AI agents are still developing, but they’re already being used in customer service, content creation, data analysis, and many other areas. The key is understanding that they’re not just smarter chatbots – they’re digital workers that can think and act on their own.
The future isn’t about replacing humans, but about humans and AI agents working together to solve bigger problems faster than ever before.
Want to learn more about AI tools and automation? Check out our other guides on building AI workflows and prompt engineering.
