One great use of agentic coding is being able to add and very granular tracing instrumentation to help with these sort of optimizations.
All of the significant server applications I have encountered in the industry have suffered from the same problem, which surprised their authors but seemed obvious to me: the application was spending the majority of its CPU time doing meta-work like entering and leaving epoll, stealing work from itself, etc. There are principles for writing Tokio servers and these are good points in the OP but I think they are little-known and too easy to violate.
I can't say I'm surprised when I see the 100+ function stack traces that Axum built on Tokio produces.
Before you say Axum is "holding it wrong" the project lives under the tokio-rs GitHub org.
Also a great way to make sure that your app spends most of its time in observability overhead. For example even the latency histogram that the OP mentions is wildly expensive.
One legitimately great thing about LLMs is that it makes it feasible to add these kind of tracing instrumentations temporarily for profiling and then throw them away so they never reach source control let alone production.
I can get an LLM to trace my incomprehensible Tokio application which was also written by an LLM, which is why I don't understand its behavior. Truly the future we were promised.
If you’re not using eBPF to trace your app you’re doing it wrong.
The low cost of eBPF tracing is another myth.