I am somehow close to what MSFT and GitHub are doing here, mostly because I believe it is a great idea, and I am experimenting on it myself.

Especially on the angle of automatic/continuos improvement (https://github.github.io/gh-aw/blog/2026-01-13-meet-the-work...)

Often code is seen as an artifact, that it is valuable by itself. This was an incomplete view before, and it is now a completely wrong view.

What is valuable is how code encode the knowledge of the organization building it.

But what it is even more valuable, is that knowledge itself. Embedded into the people of the organization.

Which is why continuos and automatic improvement of a codebase is so important. We all know that code rot with time/features requests.

But at the same time, abruptly change the whole codebase architecture destroys the mental model of the people in the organization.

What I believe will work, is a slow stream of small improvements - stream that can be digested by the people in the organization.

In this context I find more useful to mix and control deterministic execution with a sprinkle of intelligence on top. So a deterministic system that figure out what is wrong - with whatever definition of wrong that makes sense. And then LLMs to actually fix the problem, when necessary.