August 4th, 2025. Another day of debugging in the world of AI copilots. This era is a whirlwind of LLM-assisted ops and eBPF production-proven technology. I’ve spent the morning wrestling with an issue that has been driving me nuts — a performance bottleneck in one of our AI-native applications.

The story begins yesterday when we started seeing a significant drop in the response time for our eBPF-based network monitoring tool. Our platform team had integrated it to work seamlessly with Wasm containers, allowing us to run more complex and adaptive policies directly on the edge devices. The goal was to make our infrastructure smarter and more responsive, but clearly, something wasn’t working as planned.

We kicked off a triage session early this morning. After some initial checks, we realized that the issue seemed to be linked to the Wasm execution environment. It turns out that a particular Wasm module, which was designed to offload certain AI computations from our application server, was causing a significant overhead when invoked. This was unexpected, given how well eBPF had performed in similar scenarios.

One of my team members suggested using perf events and tracepoints to get more detailed insights into the Wasm execution path. We dove deep into the logs and found that there were frequent context switches between different kernel threads, which was eating up our CPU cycles. This pointed towards a potential issue with how we were handling memory mappings in the Wasm runtime.

I decided to take a step back and revisit some of the fundamental design choices we made when integrating eBPF and Wasm. I had assumed that eBPF would handle the low-level details, allowing us to focus on higher-level logic. However, it seems that our initial approach might have been too simplistic.

After a heated discussion with my colleague, we came up with a plan to refactor the Wasm module by moving some of the more computationally intensive tasks into pure eBPF code. This would not only reduce the overhead but also leverage the strengths of both technologies: the speed and efficiency of eBPF for low-level operations and the flexibility and power of Wasm for higher-level AI computations.

We spent the rest of the morning rewriting parts of our application to better align with this new strategy. By lunchtime, we had a prototype working that showed promising results. The response time for our network monitoring tool was back on track, and we were confident enough to roll it out in a smaller, non-critical environment.

This experience underscores the importance of understanding the underlying mechanics when integrating emerging technologies like eBPF and Wasm. While these tools are incredibly powerful, they require careful consideration of how best to leverage their strengths while mitigating potential weaknesses.

As I wrap up my notes for today, I can’t help but feel a mix of frustration and satisfaction. Frustration at the complexity of this problem, but also satisfaction in having made progress. This is the reality of working with AI copilots — they make our lives easier, but we still have to deal with the nitty-gritty details.

The future of platform engineering lies in mastering these technologies, even as they continue to evolve. And while the journey can be challenging, it’s also incredibly rewarding. Here’s to more adventures in eBPF and Wasm!

---

[This blog post reflects a day in the life of an engineer dealing with real-world technical issues, integrating emerging tech stacks like eBPF and Wasm, and the challenges that come with it.]