The closest I've ever felt to this as a native English speaker is reading words in music scores in English. I'm a classically trained cellist, and grew up learning notation with Italian and French words for directions and expression. I've never learned either of those languages, save the words used in music notation. Seeing a score with those words in English just feels... wrong. Not in any big way, but as you said: uncanny. Definitely get the "bad psuedocode" vibe, because to me it English in music notation feels similar -- like the person who wrote it didn't know what they were doing, even though the notation makes perfect sense and the music is good. It removes some of the flair of the art of the notation itself for me.
This reminds me of an article I read that was posted on HN only a few days ago: Uncertain<T>[1]. I think that a causality graph like this necessarily needs a concept of uncertainty to preserve nuance. I don't know whether this would be practical in terms of compute, but I'd think combining traditional NLP techniques with LLM analysis may make it so?
I don't think the part about front and back channels is quite correct. GET and POST requests are both encrypted in HTTPS -- including the URL (but not the domain, as DNS resolution happens separately). Front and back channel are more to do with trust boundaries, and what information is public vs private from the client's perspective.