Ai Agents Explained

Have you ever felt overwhelmed by the constant buzz surrounding AI agents—terms like RAG, ReAct, and agentic workflows thrown around without clear explanations? You’re certainly not alone. Most explanations online either dive too deeply into complex technical jargon or remain frustratingly superficial, leaving everyday users puzzled. But understanding AI agents doesn’t have to be daunting. In fact, if you’re already using tools like ChatGPT, you’re perfectly positioned to grasp these powerful AI capabilities and discover how they can simplify your life.

Let’s break down AI agents into three easy-to-follow levels, building on concepts you already know and use daily.

Level One: Large Language Models Simplified

You’ve probably interacted with large language models (LLMs) like ChatGPT, Google Gemini, or Claude without even realizing it. These AI chatbots are essentially powerful text generators. When you type a prompt into ChatGPT—say, asking it to write a polite email—it responds by drawing from vast amounts of data to craft the perfect reply. Simple, right?

But here’s the catch: If you asked ChatGPT when your next meeting is scheduled, it would stumble. Why? Because it doesn’t have access to your personal calendar or private data. This highlights two fundamental traits of LLMs: they have limited knowledge about your personal or internal company data, and they only react passively to your inputs.

Level Two: Understanding AI Workflows

Now, what if your chatbot could look up your calendar whenever you ask about upcoming events? This is where AI workflows come in. Think of workflows as step-by-step instructions you give an AI tool. For instance, you could instruct ChatGPT: “Whenever I ask about a personal event, first search my Google Calendar, then respond.”

This setup works wonderfully until you ask a different follow-up question like, “What’s the weather like on that day?” Since your instruction was strictly to check your calendar, the AI wouldn’t know to access weather data. Workflows, while powerful, follow only the specific paths predefined by humans. This rigid instruction set, also known as “control logic,” is their primary limitation.

Consider a real-world scenario: You compile daily news articles into a Google Sheet, summarize them using Perplexity, and then draft LinkedIn posts with Claude. This automated sequence is a clear example of an AI workflow—structured, predictable, but requiring human intervention to tweak and perfect.

Pro tip: When you hear the term “Retrieval-Augmented Generation” (RAG), don’t get intimidated. It’s simply a workflow where AI models access external data before responding, such as pulling info from your calendar or weather services.

Level Three: The Rise of AI Agents

So, what’s the leap from workflows to AI agents? Imagine handing over decision-making power entirely to AI. Instead of strictly following instructions, an AI agent independently determines the best approach to accomplish your goal. It reasons and acts autonomously.

For instance, rather than manually compiling news articles and refining LinkedIn posts yourself, an AI agent would autonomously choose the best way to gather information, summarize effectively, and then even iteratively critique its own outputs until they meet your standards. In essence, an AI agent doesn’t just follow a predefined path; it adapts, learns, and optimizes on the fly.

This ability to independently “reason and act” is encapsulated in frameworks like “ReAct” (Reason + Act). It represents the most common configuration used by AI agents today. Essentially, AI agents assess situations, decide the most effective method, execute actions using available tools, and iteratively refine their outcomes—all without human input.

Real-World AI Agent Examples

Andrew Ng, a prominent figure in AI, demonstrated this concept brilliantly. Imagine searching a video database for clips featuring a “skier.” An AI agent first reasons what a skier looks like—someone moving swiftly on skis through snow—then autonomously scans footage, identifies matching scenes, and indexes the clips accordingly. Previously, this tedious tagging process required manual human effort. Now, an AI agent handles it swiftly and efficiently.

Bringing It All Together

To summarize clearly:

• Level 1 (LLMs): Passive interaction; you input, AI responds based on training.
• Level 2 (Workflows): Structured paths defined by humans; AI executes predetermined actions.
• Level 3 (Agents): Autonomous decision-making; AI independently reasons, acts, and iterates to achieve a goal.

Understanding these distinctions isn’t just academic—it’s practical. As AI continues to evolve, becoming comfortable with these concepts means staying ahead of the curve and harnessing AI’s full potential to simplify your tasks and amplify your productivity.

So, next time someone drops buzzwords like “RAG” or “agentic workflows,” you’ll not only understand—you’ll appreciate how these powerful tools can truly transform your day-to-day life.