Wow, so that RubyLLM gem makes writing an agent more about basic IO operations. I have somehow thought there needed to be deep understanding of LLMs and/or AI APIs to build things like this where I would need to research and read a lot of docs, stay up to date on the endless updates the various AI systems have, etc. The example from the article is about files and directories, this same concept could apply to any text inputs, like data out of a Rails app.
That was my misunderstanding as well. That's why I wrote the article.
Btw, it's not even about the RubyLLM gem. The gem abstracts away the calling of various LLM providers and gives a very clean and easy to use interface. But it's not what gives the "agentic magic". The magic is pretty much all in the underlying LLMs.
Seeing all the claims made by some closed source agent products (remember the "world's first AI software engineer"?) I thought that a fair amount of AI innovation is in the agent tool itself. So I was surprised when I realised that almost all of the "magic" parts are coming from the underlying LLM.
It's also kind of nice because it means that if you wanted to work on an agent product you can do that even if you're not an AI specialised engineer (like I am not).
