AI education still feels stuck in the chatbot era

Maybe this is just me, but a lot of AI learning content feels strangely disconnected from where the field is heading. A few years ago it made sense. Most people were learning prompts, building chatbots, experimenting with APIs, and figuring out what these models could do. That was the frontier. when I look at what people are actually trying to build, it feels very different. They're building research assistants, coding agents, workflow automation, document processing systems, customer support pipelines, and all sorts of software that needs to make decisions, use tools, keep track of state, and recover when things go wrong. Yet most learning paths still stop at "here's how to prompt a model" or "let's build a chatbot." I'm not saying those skills aren't useful. They're the foundation. But if someone wants to build agents, the jump from chatbot tutorials to real systems is much bigger than most people expect. The first time I built something beyond a simple chat interface, I realized the hard problems had very little to do with prompting. The challenges were things like deciding what information the system should remember, how it should handle failures, when it should ask for human input, how to evaluate whether it was doing a good job, and how to stop it from creating a mess when connected to real tools. That feels much closer to software engineering and systems design than the way AI is often taught online. I'm curious whether others see the same gap. If somebody asked you for the best way to learn AI agents in 2026, what would you want them to learn after prompts and chatbots?