Not the parent commenter, but most of it is surprisingly simple. You basically start with a "chat app" where you have a list of messages, send the whole conversation to an LLM and it replies back, which also gets added to the same list.

And you add a small twist, that instead of a 1-to-1 back and forth, you instead put it into a loop, where the LLM reply can itself "have a turn", e.g. a tool invocation, where your system is the one that replies (e.g. with the tool invocation's result). That's pretty much it, you have a 1 to potentially many "chat".

The harder part is getting all the "soft" parts right, like how to have well-behaving tool calls, timeouts, prevent huge cycles eating up tokens, but there are no one way to solve these, it's a fundamentally heuristic-heavy area.

This comment is a great example of how large and strange the skills gap in AI is right now.

Curious why your first impulse is not simply to point your favorite agent at a few examples and start brainstorming/planning from there?

Multiple times I’ve built a purpose specific bespoke tool starting this way. In fact, it’s a great way to learn how specialized tools are built.

It's pretty simple nowadays if you know conceptually how they work. Running the LLM calls in a loop with tools is an agent. You only need 10 or so basic tools to accomplish nearly anything, and you can build a dynamic skill system from that. Look at https://github.com/patw/pengy, ignore the app look at the spec.md file, feed that to your current agent of choice and make your own version. Use whatever tech stack or UI you're comfortable with. Change some of the choices in how it works, so it fits what you want to work.

Implementing your own agent is very easy. Here is a minimal agent in 60 lines of Python without dependencies:

https://github.com/99991/MinimalAgent

You only need a single tool to start with. All recent LLMs know how to use bash for reading/writing/editing/executing.

Why would the language matter? Just use your favorite.

Look at pi coding agent.