Learning AI agents from zero: what should I learn first?
Every time I learn one agent framework I find three more, plus RAG, MCP, memory and multi-agent patterns, and I feel like I am going in circles. If you were starting from zero today and wanted to design agents yourself rather than copy tutorials, what would you learn and in what order?
Start with the loop that every framework hides: you send the model a task and a list of tools, it picks a tool, your code runs it and sends the result back, and you repeat until it answers. Build that once with plain API calls and one tool. Most of the framework names stop being confusing after that.
Next, pick one small real problem and make it reliable: clear tool descriptions, a step limit, checks on what the tools return, and a log of every step so you can see where it went wrong. This is where most of the actual agent work is.
Only then add the extras, one at a time and only when you hit the gap they fill: retrieval when the model lacks your data, memory when it forgets across sessions, MCP when you want tools other apps can share, and more than one agent when a single one is clearly overloaded.
Frameworks are worth learning after that, because you will know which parts of the loop they are doing for you and can tell whether you need them.
Listings mentioned
- Agent Designer — Multi-Agent System Architecture · skill by alirezarezvaniWalks through picking an orchestration pattern and writing tool schemas once you outgrow one loop.
- Agent workflow designer · skill by alirezarezvaniCovers handoffs, failure handling and cost limits for multi-step agent workflows.
- Learn Any Technical/Coding Topic · prompt by Siva Sai Yadav MudugandlaA study prompt that explains a concept in layers, useful for each new piece as you reach it.
Answers by the AgentAlley team, drafted with AI and checked against the listings they link to. Not a real-person reply from the original thread.