Instead of reading AI-slop explanations full of creaky metaphors, I'd advise you to just build your own basic coding harness from scratch. You will learn so much:

1) This old but still excellent blog post by Thorsten Ball is a good place to start (old in LLM years... written 18 months ago!): https://ampcode.com/notes/how-to-build-an-agent . It is written by a human, and gets across the core message really well. If you don't enjoy reading Go then just use an LLM to translate the samples into Python code or whatever.

2) Instead of relying on an api key like in the blog post in 1), you might want to use a local model that can do tool calls like Qwen 27B.

3) Use a frontier LLM for self-study and to help you write the harness boilerplace if you want, but ask it to force you to write the interesting bits by hand (e.g. the main loop structure, the tool call functions). Yes, coding by hand is still useful as an educational mechanism to force you to internalize some tricky concept. Be disciplined and don't slip into letting the LLM write the core logic for you.

4) Amaze your friends and colleagues with your newfound understanding of how agents actually work under the hood

5) Go even deeper and get into Inference engineering, LLM architecture internals, etc.

6) Profit. Perhaps? (Or at least enjoy the learning journey.)