eBPF in Go: Observability for AI-Generated Services

eBPF in Go: Observability for AI-Generated Services

A hands-on tutorial on using eBPF with Go for kernel-level observability to debug production issues in AI-generated services.

I recently hit a wall debugging a Go service that was generating AI code. P95 latency jumped from 40ms to 4 seconds with no app-level visibility into what was happening. Traditional logging and profiling tools were useless - the issue was happening at the kernel level.

AI-generated code often lacks context about kernel interactions. eBPF lets you trace:

All without modifying your kernel or restarting services.

package main

import ("fmt""os""  ")

func main() {// Load the eBPF programobjs := &struct {TraceOpen *ebpf.Program `ebpf:"trace_open"`}{}

collSpec, err := ebpf.LoadCollectionSpec("trace.pbf")
if err != nil {
    panic(err)
}

if err := collSpec.LoadAndAssign(objs, nil); err != nil {
    panic(err)
}

// Attach to the open syscall
kp, err := ebpf.Kprobe("do_syscall_64")
if err != nil {
    panic(err)
}

if err := objs.TraceOpen.Attach(kp); err != nil {
    panic(err)
}

fmt.Println("Tracing... Hit Ctrl-C to exit")
<-make(chan struct{})
}

This approach helped me identify that AI-generated services were making excessive file I/O calls that weren’t visible in application logs. Once we added eBPF tracing, we could see the actual kernel-level behavior and optimize accordingly.

I’ve published a complete working example with step-by-step instructions:

https://cheikhhseck.medium.com/ebpf-in-go-observability-for-ai-generated-services-9aae7573b823