Learn, check, apply, repeat: what prépa taught me about AI agents

My preparation classes taught me a rhythm that I still use: learn, check, apply, repeat. Understanding a lesson was not enough. I had to check what I had actually learned, use it in an exercise and return to whatever was still unclear. I see that same rhythm in how I build AI agents.

A large problem can look impossible when I treat it as a single block. I prefer to break it into small steps that I can verify. What information do I need? What should this step produce? How will I know it worked? These simple questions help me move forward without confusing speed with understanding.

I build my agents in the same way. I give each step a clear role, check what it produces and only then move on. If something is wrong, I return to that step rather than adding instructions everywhere. I want to understand the error, not hide it behind another attempt.

Learning for 30 minutes a day is now part of the job. I do not see it as collecting tools. I want to learn something useful, check that I understand it and apply it to my work. Then I start again. This rhythm keeps me curious without losing the thread.