AI agents are where software is going. Soon every product you use will have one inside it, and the companies that visit your campus are already building them. In interviews this year they are not asking what an LLM is. They are asking how you would build an agent: the loop, the tools, the memory, and what happens when it fails. They do not want you to start from the basics. They expect you to know these parts already. And once you do, building an agent is easy. Frameworks change every few months; the loop underneath has not changed since 2022.
01Agent foundations
02Memory and planning
03Frameworks and runtimes
04Benchmarks, interfaces, and observability
05Reliable production agents
06Agent workbench foundations
07Workbench operations and capstone
08Agent-assisted engineering
09Product judgment and delivery
Nine modules, in the order a working engineer would need them: first the loop with no framework at all, then memory and planning, then the frameworks everyone names in interviews, then testing, safety, and shipping agent work inside a real codebase.
A situation you already know, from daily life, so the problem is obvious before any jargon.
The idea, in plain words, with a flow you can see.
A small Python program, standard library only. Run it, then break something on purpose and watch what happens.
Where you will meet this in real products, and the mistakes people make with it.
The interview questions you are likely to get, with model answers, then a short quiz.
One lesson a day, about an hour each. Fifty-four lessons, in order. Mark a lesson done only when you can explain it to a friend without looking.