Context has never been the bottleneck for me. AI just stops working when I reach certain things that AI doesn't know how to do.
I've been experimenting with Github agents recently, they use GPT-5 to write loads of code, and even make sure it compiles and "runs" before ending the task.
Then you go and run it and it's just garbage, yeah it's technically building and running "something", but often it's not anything like what you asked for, and it's splurged out so much code you can't even fix it.
Then I go and write it myself like the old days.
It's context all the way down. That just means you need to find and give it the context to enable it to figure out how to do the thing. Docs, manuals, whatever. Same stuff that you would use to enable a human that doesn't know how to do it to figure out how.
I treat "uses AI tools" as a signal that a person doesn't know what they are doing
I imagine this has to to with concurrency requiring conceptual and logical reasoning, which LLMs are known to struggle with about as badly as they do with math and arithmetic. Now, it's possible that the right language to work with the LLM in these domains is not program code, but a spec language like TLA+. However, at that point, I'd probably just spend less effort to write the potentially tricky concurrent code myself.