Have you ever used an AI chatbot to draft an email or answer a quick question? If so, you’ve already experienced the power of artificial intelligence. But what if AI could do even more? What if it could not just answer questions, but also figure out what to do next, reason through problems, and even critique its own work without you telling it every step? That’s where AI agents come in.
For many of us who use AI tools regularly but don’t have a technical background, the idea of AI agents can seem complicated. You often hear terms like “RAG” or “ReAct” that sound intimidating. But the truth is, understanding AI agents is simpler than you think. This guide is for people like you: everyday AI users who want to grasp enough about these advanced systems to see how they’ll impact your life and work. We’ll start with what you already know about chatbots, move to AI workflows, and finally explore the exciting world of AI agents, using real-world examples you’ll actually encounter.
Level 1: Large Language Models (LLMs)
To understand AI agents, we first need to start with the basics: Large Language Models, or LLMs. Think of popular AI chatbots like ChatGPT, Google Gemini, and Claude. These are all applications built on top of LLMs. At their core, LLMs are powerful AI models trained on vast amounts of text data, making them excellent at generating and editing written content.
The interaction with an LLM is straightforward. You, the human, provide an input (often called a “prompt”), and the LLM produces an output based on its training data. For example, if you ask ChatGPT to draft an email politely requesting a coffee chat, your request is the input, and the polished email it generates is the output. It’s a simple give-and-take.
However, LLMs have important limitations. Imagine asking ChatGPT, “When is my next coffee chat?” Even without seeing its response, you know it’ll likely fail. Why? Because it doesn’t have access to your personal calendar or proprietary information. This highlights two key traits of LLMs:
- Limited Knowledge of Private Data: While LLMs are trained on massive public datasets, they don’t know your personal details, internal company data, or other private information.
- Passive Nature: LLMs are reactive. They wait for your prompt and then respond. They don’t take action or initiate tasks on their own.
These two traits are crucial to remember as we move forward. LLMs are fantastic for text generation and editing, but they’re just the first step in the journey toward more autonomous AI capabilities.
Level 2: AI Workflows
Building on our understanding of LLMs, let’s explore AI workflows. What if you could give your LLM a set of instructions? For instance, what if you told an AI, “Every time I ask about a personal event, first search my Google Calendar to get the data, and then give me a response”?
With this logic in place, the next time you ask, “When is my coffee chat with Elon Husky?”, the LLM could actually give you the correct answer. It would first go into your Google Calendar to find that information before providing a reply. This is the essence of an AI workflow: it follows a predefined path set by a human. This path is often called the “control logic.”
But here’s a crucial point: workflows can only follow the specific paths they’re given. What if your next question is, “What will the weather be like that day?” The LLM would fail to answer because the path you defined only instructs it to search your Google Calendar, which doesn’t contain weather information. If the specific steps aren’t explicitly built into the workflow, the AI can’t deviate from its instructions.
You can add many steps to a workflow. For example, you could enhance it to access weather information via an API and then use a text-to-audio model to speak the answer aloud. No matter how many steps you add, or how complex the sequence, as long as a human defines those steps and remains the ultimate decision-maker, it’s still an AI workflow.
A term you might have heard in this context is Retrieval Augmented Generation (RAG). While it sounds complex, RAG simply refers to the process where an AI model looks up information from external sources (like your calendar, a database, or a legal document) before generating its answer. Essentially, RAG is a type of AI workflow designed to give LLMs access to specific, up-to-date, or proprietary information they weren’t originally trained on.
Let’s look at a real-world example. Following a helpful tutorial by Helena Louu, I created a simple AI workflow using make.com. This workflow automates creating social media posts from news articles:
- Compile News Links: First, I gather links to news articles and store them in a Google Sheet.
- Summarize Articles: Next, I use Perplexity to summarize those news articles.
- Draft Social Media Posts: Then, using a specific prompt I wrote, I instruct Claude to draft LinkedIn and Instagram posts based on the summaries.
- Schedule Automation: Finally, I schedule this entire sequence to run automatically every day at 8 AM.
This is a classic AI workflow because it follows a precise, predefined path that I, as the human, set. If I test this workflow and don’t like the final output (for example, the LinkedIn post isn’t funny enough), I have to manually go back and rewrite the prompt for Claude. This trial-and-error, where a human makes the adjustments, is a defining characteristic of AI workflows.
Level 3: AI Agents
Now, let’s move to Level 3: AI Agents. This is where things get truly exciting because the AI itself becomes the decision-maker.
Think back to our social media post creation example using make.com. As the human decision-maker, I had two main responsibilities:
- Reason (Think): I had to decide the best approach. Should I compile news articles, then summarize them, and then write the final posts? This involves figuring out the optimal sequence of steps.
- Act (Do): I had to use specific tools to carry out these steps. This meant finding and linking news articles in Google Sheets, using Perplexity for real-time summarization, and using Claude for copywriting.
The single biggest change that transforms an AI workflow into an AI agent is when the Large Language Model (LLM) replaces the human decision-maker. In an AI agent, the LLM itself must reason and act autonomously to achieve a given goal.
For instance, an AI agent tasked with creating social media posts would first reason: “What’s the most efficient way to compile these news articles? Should I copy and paste each article into a Word document? No, it’s probably better to compile links to those articles and then use another tool to fetch the data. Yes, that makes more sense.” Then, it would act using available tools. “Should I use Microsoft Word to compile links? No, inserting links directly into rows in Google Sheets is much more efficient, especially since the user has already connected their Google account with make.com.”
This combination of reasoning and acting is so fundamental to AI agents that the most common framework for them is called ReAct. It stands for “Reasoning” and “Acting,” simplifying what an AI agent does.
A third key trait of AI agents is their ability to iterate and self-improve. Imagine our social media post example again. When I tested the workflow earlier, I had to manually rewrite the prompt to make the LinkedIn post funnier. This iterative process of trying, checking, and refining was done by me, the human. An AI agent can do this autonomously.
In our example, an AI agent might draft a LinkedIn post. Then, it would autonomously add another internal LLM to critique its own output. It might “think,” “I’ve drafted version 1 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. And I’ll repeat this until all the best practices criteria are met.” After a few cycles of this self-correction, the AI agent produces a refined final output without human intervention for each iterative step. This autonomous iteration is a powerful aspect distinguishing AI agents from simple workflows.
Real-World AI Agent Examples to Illustrate Concepts
To truly grasp AI agents, concrete examples are invaluable. Andrew Ng, a prominent figure in AI, created a demo website that beautifully illustrates how an AI agent works in practice.
In his demonstration, when a user searches for a keyword like “skier,” the AI vision agent in the background does several things:
- Reasoning: It first reasons about what a skier looks like (e.g., “a person on skis going fast in the snow”).
- Acting: Then, it acts by scanning video footage, identifying clips it believes contain skiers, indexing those clips, and returning them to the user.
While this might not feel incredibly impressive at first glance, remember that an AI agent did all of that automatically. A human didn’t have to manually review every minute of footage, identify skiers, and add tags like “skier,” “mountain,” or “snow.” The AI agent handled the entire decision-making and action process. Naturally, the programming behind the scenes is far more technical than what we see on the surface, but that’s precisely the point for most users: we want a simple application that works without needing to understand its intricate backend operations.
Speaking of examples, I’m also currently building my own basic AI agent using Nan. If you have ideas for what kind of AI agent you’d like me to create a tutorial on next, please let me know in the comments below.
Summary: The Three Levels of AI Interaction Simplified
Let’s recap the three levels of AI interaction we’ve explored, simplifying them for clarity:
| Feature | Level 1: Large Language Models (LLMs) | Level 2: AI Workflows | Level 3: AI Agents |
|---|---|---|---|
| Interaction | Input from human, LLM provides output | Input from human, LLM follows predefined path | Goal from human, LLM reasons, acts, and iterates autonomously |
| Key Trait | Passive; responds only to prompts | Human designs and controls the execution path | LLM is the decision-maker; performs reasoning and action |
| Capabilities | Generates/edits text based on training data | Retrieves info from external tools; follows strict instructions | Plans, executes tasks via tools, observes results, self-corrects |
| Example | Chatbot drafts an email | Chatbot checks calendar for event | AI autonomously creates and refines social media posts |
In essence:
- Level 1 LLMs are about input leading to output. They’re great at generating and editing text but lack external knowledge or passive decision-making.
- Level 2 AI Workflows add a predefined pathway. You tell the LLM exactly what steps to take, which might involve using external tools to gather information. The human programs the path for the LLM to follow.
- Level 3 AI Agents represent a significant leap. You give the AI a goal, and the LLM itself figures out the best way to achieve it. It performs reasoning, takes action using tools, observes the results, and decides whether more steps or iterations are required. The key is that the LLM is the decision-maker in the workflow, autonomously achieving the initial goal.
If this was helpful, you might also find it useful to learn how to build a prompts database in Notion. Stay tuned for more insights and tutorials!
Additional Resources and How to Stay Updated
At DXTech, we’re focused on helping small and growing businesses unlock the full potential of AI. Whether you’re just starting with automation or looking to take the next step with AI agents, here’s how you can stay ahead:
- Explore our AI use cases: We regularly share real-world examples of how AI workflows and agents are being applied in sales, marketing, operations, and support — especially for small teams with limited resources.
- Learn with us: Our internal knowledge base and client workshops are built to simplify AI concepts, showing you exactly how to apply them in your day-to-day business without needing a technical background.
- Follow our updates: We keep our blog fresh with practical guides, behind-the-scenes case studies, and tips from our own projects — all focused on actionable value, not hype.
At DXTech, we don’t just talk about AI — we build it into real business processes. If you’re interested in how AI agents can work for your business, stay connected with us and keep learning together.