AI Agents Explained In 5 Minutes For Non-Technical Readers |

Most explanations of AI agents are either overly technical or too simplistic. This article is for those who use AI tools regularly but have no technical background and want to understand just enough about AI agents to see how they will be affected.

In this article, we'll follow a simple one-two-three learning path. We'll build on concepts you already understand, like chatbots, then move on to AI workflows, and finally, AI agents. All the while, we'll use examples you might encounter in real life. You'll find that intimidating terms like RAG or ReAct are much simpler than they seem.

Level 1: Large Language Models (LLMs)

Popular AI chatbots like ChatGPT, Google Gemini, and Claude are applications built on top of Large Language Models (LLMs). They are fantastic at generating and editing text.

Here’s a simple visualization: a human provides an input, and the LLM produces an output based on its training data. For example, if you ask an LLM to draft an email requesting a coffee chat, your prompt is the input, and the resulting polite email is the output.

Simple enough. But what if you asked the chatbot, "When is my next coffee chat?" It would fail because it doesn’t have access to your personal calendar. This highlights two key traits of LLMs:

Limited Knowledge: Despite being trained on vast amounts of data, they have limited knowledge of proprietary information like personal data or internal company data.
Passive Nature: LLMs are passive; they wait for a prompt and then respond.

Keep these two traits in mind as we move forward.

Level 2: AI Workflows

Let's build on our example. What if a human told the LLM, "Every time I ask about a personal event, perform a search query and fetch data from my Google Calendar before providing a response."

With this logic implemented, the next time you ask, "When is my coffee chat with Elon Husky?" you'll get the correct answer because the LLM will first check your Google Calendar.

But here's where it gets tricky. What if your next follow-up question is, "What will the weather be like that day?" The LLM will fail because the path we instructed it to follow is to always search the Google Calendar, which doesn't contain weather information. This is a fundamental trait of AI workflows: they can only follow predefined paths set by humans. This path is also technically known as the "control logic."

Pushing the example further, what if we added more steps? We could allow the LLM to access the weather via an API and then use a text-to-audio model to speak the answer: "The weather forecast for seeing Elon Husky is sunny with a chance of being a good boy."

No matter how many steps we add, this is still just an AI workflow. Even with hundreds or thousands of steps, if a human is the decision-maker, there is no AI agent involvement.

Note: Retrieval-Augmented Generation (RAG) is a fancy term you'll hear often. In simple terms, RAG is a process that helps AI models look things up before they answer, like accessing a calendar or a weather service. Essentially, RAG is just a type of AI workflow.

A Real-World Workflow Example

Here’s a simple AI workflow built with an automation tool:

Compile Data: First, use a tool like Google Sheets to compile links to news articles.

| Article Link | | :--- | | https://news.example.com/article1 | | https://news.example.com/article2 |
Summarize: Use an AI tool like Perplexity to summarize those news articles.
Draft Content: Use another AI like Claude with a specific prompt to draft a LinkedIn and Instagram post based on the summaries.
Schedule: Finally, schedule this workflow to run automatically every day at 8 a.m.

This is an AI workflow because it follows a predefined path set by a human: Step 1, do this; Step 2, do this; Step 3, do this. If the final output isn't quite right (e.g., the LinkedIn post isn't funny enough), a human has to manually go back and rewrite the prompt for the drafting step. This trial-and-error iteration is done by a human.

Level 3: AI Agents

Continuing the automation example, let's break down the human's role. With the goal of creating social media posts, the human decision-maker needs to:

Reason: Think about the best approach (compile articles, summarize them, then write the posts).
Take Action: Use tools to execute the plan (Google Sheets for links, Perplexity for summaries, Claude for copywriting).

The one massive change that transforms this AI workflow into an AI agent is replacing the human decision-maker with an LLM.

In other words, the AI agent must reason. * What’s the most efficient way to compile these news articles? Should I copy and paste each article into a document? No, it’s probably easier to compile links and use another tool to fetch the data. That makes more sense.

The AI agent must also act (i.e., do things via tools). * Should I use Microsoft Word to compile links? No, inserting links directly into rows is more efficient. What about Excel? The user has connected their Google account, so Google Sheets is a better option.

Note: Because of this structure, the most common configuration for AI agents is the ReAct framework. All AI agents must Reason and Act—so ReAct is a simple concept once broken down.

A third key trait of AI agents is their ability to iterate. Remember when the human had to manually rewrite the prompt to make the LinkedIn post funnier? An AI agent can do the same thing autonomously.

In our example, the AI agent would autonomously add another LLM to critique its own output. * "Okay, I've drafted V1 of a LinkedIn post. How do I make sure it's good? I know, I'll add another step where an LLM critiques the post based on LinkedIn best practices. Let's repeat this until the best practices criteria are all met." After a few cycles, we have the final output.

A Real-World Agent Example

Consider a demo website that illustrates how an AI agent works. When a user searches for a keyword like "skier," the AI vision agent in the background performs several steps:

Reasoning: It first thinks about what a "skier" looks like (e.g., a person on skis, moving fast in the snow).
Acting: It then looks at clips in its video footage library, trying to identify what it thinks a skier is.
Indexing & Returning: It indexes the relevant clip and returns it to the user.

Although this might not seem impressive on the surface, remember that an AI agent did all of that instead of a human having to review the footage beforehand, manually identify the skier, and add tags like "skier," "mountain," and "snow." The programming is obviously more complex, but the point of such a system is to provide a simple app that just works, without the average user needing to understand the backend processes.

Summary: The Three Levels

Here’s a simplified visualization of the three levels we covered: