I’ll never forget the first time I watched a system fail spectacularly during a Black Friday sale. Everything looked fine in testing—our load tests passed, performance benchmarks were green, and we’d even done a few "chaos engineering" experiments. Then real traffic hit, and within 30 minutes, the entire checkout system was down. The worst part? Looking at the logs afterward, we could see warning signs building for hours before the collapse. We just didn’t know what to look for; our dashboards showed isolated metrics in neat rows, but they lacked the ability to correlate rising queue depth with creeping database lock times, memory-pressure warnings, and subtle shifts in user traffic. Without trend correlation or root-cause prioritization, the early signals were buried in the noise, so by the time we noticed, it was already too late.