This is the 1-minute version of the 45-minute workshop I delivered yesterday. An AI agent starts with something simple. An LLM. And an LLM is basically a text prediction machine. It guesses the next word based on probabilities. When I type “the quick brown fox” you will see the model pick “jumps” with the highest probability. Same when I type “the president of the US is” you will see it predict “Donald…” and continue from there. (Here is the link to the next-word predictor app: https://lnkd.in/dUiDT2BZ) This is simplified, but this is how it works. One token at a time. On their own, LLMs are static. They do not do anything. They just predict text. An AI agent is what happens when you take this LLM and you attach tools to it. For example, the model can run a web search tool. Or call an API. Or trigger an action. Then it reads the results. Decides what to do next. Maybe calls another tool. And keeps iterating until it solves the task. Agents also include instructions and memory. Short term and long term. So in a nutshell. An AI agent is an LLM combined with tools and reasoning loops. It turns a text predictor into a system that can plan, act, and solve real tasks. #aiagents #ai

It can start with one simple step. Go to ChatGPT, Claude, or any other LLM provider. Type something like: “Teach me AI.” Then use a prompt that: • asks about your current level • understands your goals • and builds a personalized learning plan based on your answers From there, the idea is simple… but not easy. It requires proactivity, effort, and a lot of testing. It means treating the model like a sparring partner. You ask. You try. You build. You break things. And you run everything through your own critical thinking. That’s how you actually learn AI.

You don’t. You learn it by doing projects… and apparently by teaching it. Teaching is one of the best ways to learn, as I found out lately. Right now, I’m building a small AI agents workshop. Everything from the fundamentals of AI, generative AI, and large language models… to the practical stuff like tools, planners, and memory inside agents. And while I’ve been building this material, something interesting happened. Explaining the fundamentals forced me to revisit them. Connect the concepts again. Understand them more deeply. I literally learn better by teaching. And I became a better gen AI engineer in the process. If you’re passionate about a domain, or you want to level up, creating a practical tutorial or a workshop is one of the most effective things you can do. Blend a little theory with hands-on practice. Add a small project. And suddenly you see the subject with new clarity. Because teaching forces you to think deeply. Simplify complex ideas. Explain them clearly. And that process makes you better at the skill itself. And the bonus? You help others grow with you. Everybody levels up.

But are you still using your human skills? AI is important. AI is here, and of course we want to use it to automate things and make life easier. But sometimes we forget something simple. We still need our human side. Connecting with people in the real world. Spending time with family and friends. Taking a walk in nature, like I am right now. (atm of writing this) Reading. Breathing. Living. Life is not only about productivity, automating everything, or sitting in front of a screen all day. And it is definitely not about scrolling for hours. AI should be a tool that helps us, not something that replaces our humanity. So this is your reminder. If you are scrolling right now, take a break. Go for a walk. Call someone you care about. Do one small thing in the real world today. Your human being also needs your attention.

With models like Gemini 3, the new NanoBanana Pro, and the latest ChatGPT 5.1 tools, there is almost no problem when you ask “How can I do that?”. Right now I am creating an AI agents workshop, and I can clearly see how powerful these models are becoming. And it made me realize something. Very soon the important question will not be how we do things. AI will solve that part. The real question will be why we do certain things and whether we have clarity about what we actually want. AI is making “how to do it” irrelevant. Your why and your what will matter more.