June 30, 2025. Another day in the life of a platform engineer. I woke up with a groan, coffee machine already on. The world outside was waking up too—new headlines blinking across my morning newsfeed. One story stood out to me: “My AI skeptic friends are all nuts.” I chuckled at that, knowing firsthand how quickly perspectives shift in the tech world.

Last night, during our platform’s late-night health check, my AI copilot had flagged a potential issue with our eBPF-based network monitoring tools. At first, I dismissed it as a false positive, but something about its confidence score nagged at me. Maybe it was the fact that the flag came from an LLM-assisted ops tool, or maybe it was just another sign of how pervasive AI has become in my daily work.

I decided to investigate further, opening up the eBPF trace and feeding it into our AI-native copilot again. The tool ran its analysis and produced a detailed report, including probable root causes and potential fixes. I scrolled through the documentation, trying to find any hints that might explain this behavior. After all, I had built some of these tools myself.

The copilot’s output was both reassuring and unsettling. It suggested that there might be an issue with how we were handling packet capture buffers in our network stack. That felt like a low-level detail, something that shouldn’t be causing such a high-confidence alert. But then again, AI-native tools have been known to find things I’ve overlooked.

I decided to set up a small experiment: run the same trace on my own machine without any copilot intervention. Just as before, I couldn’t find anything amiss. Maybe it was time to trust the machine more than myself this once. After all, wasn’t that part of the promise of AI—extending human capabilities rather than replacing them?

I rolled out a small fix based on the copilot’s suggestions and watched as our health checks started reporting back with improved metrics. The confidence score had dropped from 95% to 85%, which felt like progress, but also like there was still something not quite right.

Reflecting on this experience, I realized how deeply intertwined AI is becoming in my work. We’ve gone beyond just using it as a tool; it’s now an integral part of the decision-making process. It’s no longer about me versus AI—it’s more like we’re working together, with the copilot assisting rather than competing.

As I typed up my notes for our next team meeting, I couldn’t help but think about Bill Atkinson and all he has contributed to the tech world. His passing is a reminder that despite how much the landscape changes, there are still constants—like the human element in building and maintaining systems.

The day went on with more debugging sessions, planning meetings, and occasional chats with our AI copilot. It’s strange how it feels like having another pair of eyes looking over my shoulder now. Yet, isn’t that what we all want? A partner who can spot things we might miss?

At the end of the day, as I shut down my machine and prepared to leave for home, I felt a mix of relief and excitement. Relief because our network was stable; excitement because there were still so many ways AI could enhance how we operate.

I looked out the window at the sunset, thinking about the future. It’s an era where AI isn’t just hype but reality—where it’s part of the fabric of my daily work. And who knows? Maybe in a few years, I’ll be writing another blog post about how we’ve further integrated AI into our processes.

Until then, here’s to the journey and the adventures that come with it.