Title: Debugging AI Copilots in 2025

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January 6, 2025. It feels like just yesterday I was debugging some of the first containerized microservices in a Kubernetes cluster. Now, it’s the era of AI copilots and platform teams owning entire pipelines from data ingestion to model deployment. I find myself staring at one such copilot—this time it’s not a piece of code, but an AI agent that claims to help with infrastructure monitoring.

The Setup

The latest project is a SaaS platform built on Kubernetes clusters across multiple clouds. We’ve got eBPF and Wasm deeply embedded in our stack for performance optimization. Our monitoring suite uses these tools along with several LLMs (Language Models) from various vendors, promising real-time insights into application health and performance.

One of the key features is an AI copilot that automatically suggests optimizations based on anomaly detection in system logs. It’s supposed to be a game-changer. But yesterday evening, it was flagging false positives like a broken record.

The Debug

I started by looking at the logs. The agent had flagged a service named web-app-21 as having high latency. I logged into the cluster and found that yes, there were indeed some spikes in response times, but not as dramatic as the AI was reporting. Digging deeper, I checked the network trace using eBPF. It showed a few packets being dropped due to a misconfigured route table.

However, the real culprit was something unexpected: an issue with our Wasm module that handles load balancing. A recent change in the agent’s logic caused it to send requests to a different service than intended, leading to a cascade of latency issues. The copilot had not caught this because its training data didn’t cover edge cases like this.

The Fix

I spent a few hours refactoring the Wasm module and adjusting the agent’s logic to better handle these scenarios. Once I pushed the changes, the copilot started flagging real issues instead of false positives. But more importantly, it helped us identify a subtle bug in our load-balancing algorithm that was causing intermittent delays.

The Reflection

This experience underscores how much AI has changed not just how we code but also how we approach debugging. While these tools are powerful, they’re only as good as the data and logic behind them. Debugging an AI copilot is different from debugging a piece of software—there’s this additional layer of complexity where understanding the model’s training process and its limitations becomes crucial.

In my defense, I often found myself thinking, “Why didn’t it just use better algorithms?” But then I remember how many variables go into training these models. It’s a reminder that in the era of AI copilots, we need to be more mindful about the assumptions we make and the data we feed them.

The Future

As we continue to integrate more AI tools into our workflows, it will be important for platform teams like mine to have a deep understanding not just of how these tools work but also what they can’t do. This is part of the reality of working in an era where AI is no longer just hype—it’s becoming a fundamental part of our infrastructure.

For now, I’m happy with the outcome. The copilot is more accurate, and we’ve fixed some underlying issues that could have caused bigger problems down the line. But as always, there’s room for improvement. Next time, I hope to have the AI help us find the edge cases before they become full-blown issues.

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This was a day in my life, debugging an AI copilot and learning from it. It’s both humbling and exciting to be on this journey of integrating such powerful tools into our daily operations.