Systems | Development | Analytics | API | Testing

A customer emailed on a Tuesday: checkout hung for ten seconds. I opened our tracing tool, punched in the time window, and got nothing. The trace was sampled out. We keep 1% of traces, like most shops with real traffic do. The one request that actually mattered was in the 99% we threw away. I spent twenty minutes admiring our observability stack before admitting it couldn’t answer a first-grader’s question: what happened to this person? Here’s what I know now.

Having maintained a CodePush fork for 18 months and served billions of updates, we decided it was time for an overhaul. We’re big fans of CodePush, and it was a great benefit to the React Native community. But, it was written in 2015 and had some weaknesses. The biggest of these was architectural, with limitations in how update checks and release metadata could scale. The result, Codemagic Patch, is now public and available to self-host.

It was 2am and I was paying for the privilege. Something was on fire in production, and I’d done the modern thing: I pointed an AI agent at it. It ingested the dashboards. It read the logs. It walked the traces. Then it handed me back a beautifully formatted paragraph that said, in effect, “latency is elevated on the checkout path.” I knew that. The page told me that.

In the first experiment, I wanted a baseline: if an AI coding agent gets the same production signal a human would get, can it fix bugs in a codebase it has never seen? Yes, but only when I gave it better context. With only an alert, the agent passed 51% of the runtime tests. When I added captured traffic, the actual request and response for the failing call, it climbed to 77%. This post is the second pass.