That sounds like Julia needs some kind of test suite, that lives alongside the major libraries used in Julia, and verifies that things that interact do so in a reasonable manner e.g. things that implement addition obey the rules of addition, etc, etc.
With a little reflection and some automation, such a tool could be taught to test new Julia libraries and catch problems early.
i.e. what I am talking about is effectively informally encoding protocols/interfaces and suchlike in a test-suite, rather than seeking to make it a part of the compiler/language.
Not as sexy, no doubt, but flexible and with a short time to get feedback.
This is precisely what we did with the SciML ecosystem. You can see a blog post from a year back: https://sciml.ai/news/2022/10/08/error_messages/. There's a lot of high level interface checking that goes on now through a trait system to ensure that the pieces are solvable before hitting code, and throwing high level error messages back based on those.
Over the last year we've been growing the domain of these as well, and have seen a decrease in bug reports come from it.
i.e. what I am talking about is effectively informally encoding protocols/interfaces and suchlike in a test-suite, rather than seeking to make it a part of the compiler/language.
Not as sexy, no doubt, but flexible and with a short time to get feedback.