2025¶

What Is an AI Agent? Clear Definition With Real Examples

The 2025 AI trend: AI agents

You may have thought: "Another new buzzword." Honestly, I get it.

A few months ago, everyone was talking about RAG — the AI that was supposedly going to revolutionize everything and replace entire workforces with knowledge bases. Now it's AI agents, presented as the inevitable next step.

In reality, this is yet another AI technology, and there's a push to convince you that you absolutely need it. For the record, I get along perfectly well without an AI agent that makes my coffee, cooks my meals, and tidies my apartment. But — and there's always a but — these AI agents genuinely solve real problems and address real business needs.

So, what exactly is an AI agent? What does agentic AI actually mean? To understand that, you first need to understand what ChatGPT is — and more importantly, what its limitations are. Because AI agents exist to address (or work around) the limitations of language models like ChatGPT, Gemini, and Claude.

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What Is RAG? Definition, How It Works & Real Limits

Introduction to RAG (Retrieval-Augmented Generation)

Everyone has heard of RAG (Retrieval-Augmented Generation) at this point. But what is RAG exactly? Many people have already implemented it — sometimes with no-code tools or Python libraries like LangChain or LlamaIndex. It's straightforward to set up, but I also see a lot of people disappointed with the results. The thing is, you really need to understand what it's for and how it works before you can tell whether it's the right fit for your use case.

I hadn't originally planned to write another RAG explainer — there are already plenty of resources out there. But talking with people who want to use it in enterprise contexts, I keep noticing the same pattern: everyone rushes past the fundamentals. What does RAG actually do? How does it really work in practice?

So let me walk through the points I usually end up clarifying when someone asks me about it.

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