On a recent Monday morning, I woke up to an early flight. Not literally flying an airplane—just my usual 5 AM start time, but this day felt different. My AI copilot, or rather, my LLM assistant, was running circles around me in our internal tooling for monitoring and debugging. It had been a quiet night, with no apparent issues, but as I started to review the dashboard, it was clear something wasn’t right.

The usual patterns of traffic and latency were off. The AI copilot had flagged an anomaly on my first pass through the data. “Potential issue in the East region,” it stated with a confidence that made me feel a bit like I was being lectured by HAL from 2001: A Space Odyssey. My initial response? “Really? Let’s see.”

I dove into the dashboard, and sure enough, the East region was showing higher-than-normal error rates. The copilot had already created a ticket and suggested an investigation. I clicked through to view more details, but something didn’t feel right. It’s one thing for AI to suggest tickets; it’s quite another to have it second-guess my own experience.

I started to walk through the logs manually, just like I always do. The AI copilot was right about the anomaly, but its analysis seemed overly aggressive. The East region typically handles a surge of traffic every week due to some internal optimizations we’re rolling out. My initial reaction: “Maybe it’s just the optimization kicking in?”

I spent an hour going through logs and metrics, cross-referencing with other regions where things were behaving as expected. The copilot kept chiming in with more insights, suggesting possible causes. I was starting to feel like a kid on a scavenger hunt, following clues that didn’t quite fit.

After another round of manual review, it dawned on me: the East region’s performance had been good for weeks, but suddenly there were spikes. Could something have changed in our infrastructure? A quick check through recent changes revealed nothing obvious. It was like searching for a needle in a haystack, and I felt my frustration rising.

I decided to take a step back and look at the bigger picture. The copilot had done an excellent job of pointing out potential issues, but it seemed to lack context from my experience. I fired off a message to our team: “Hey folks, any ideas on why East region is showing such high error rates? Copilot suggests possible issue but feels like there might be something else going on.”

The response was immediate and varied. Some suggested checking cloud costs, others thought it could be related to recent updates in our billing system. I decided to cross-reference with another colleague who had been working closely with the East region for months. She pointed out that we had recently added a new service there, which could potentially affect performance.

With this new insight, I revisited the logs and found something subtle: increased latency was correlated with requests from that particular service. I dug deeper into its configuration and realized a recent change in how it handled retries might be causing some of the issues.

Once identified, fixing the issue was straightforward—just tweaking the retry logic to handle errors more gracefully. The copilot’s original suggestion had pointed me towards this area, but its analysis wasn’t quite there yet. This experience taught me that while AI assistants are incredibly helpful, they still need human oversight and context.

By Wednesday afternoon, things were back to normal. I sent out a quick update: “East region issue resolved—retry logic tweak did the trick. Copilot was on track, just needed some real-world context.”

This week reminded me of the importance of combining AI insights with human judgment. While tools like copilots and LLM assistants are incredibly powerful, they still have limitations. The best results come from a blend of machine learning and human experience.

As I packed up my desk for the day, I couldn’t help but think about how far we’ve come in just a few years. From AI skeptics to full-blown copilots, the tech landscape has shifted dramatically. But at its core, engineering remains fundamentally human work—no matter how many AI helpers we have.

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