You've built fast feedback systems. Your alerts fire in minutes. Your on-call team responds within 15 minutes. Your MTTR is down to 45 minutes. But the same type of incident keeps happening. Last quarter: database connections exhausted. This quarter: database connections exhausted. Last year: API timeout cascade. This
You've decided that fast feedback matters. You know that MTTR and learning culture drive improvement. Now: how do you actually build it? Most organizations have some monitoring. Dashboards exist. Alerting rules are configured. But there's a gap between "we have monitoring" and "we
You've shipped a feature to production. For three days, nobody knows there's a problem. Your monitoring doesn't catch it. Your alerting is silent. Your customers start complaining in support tickets. By the time you discover the issue, it's been in production for
DORA's four metrics (deployment frequency, lead time, change failure rate, time to restore) are foundational. But they're outcome metrics. They tell you that something is wrong, not where it's broken. To diagnose constraints, you need diagnostic metrics. These measure the why—the underlying bottlenecks
You know your deployment frequency is slow. You know lead time is long. But you don't know why. Most organizations skip the diagnosis step and jump straight to solutions. "We need CI/CD." "We need trunk-based development." "We need platform engineering." Sometimes
You've implemented everything in The DevOps Handbook. CI/CD pipelines. Trunk-based development. Automated testing. Kanban boards. Your team is following best practices religiously. Yet you're still slow. Meanwhile, other teams deploying with fewer tools are shipping faster. The difference isn't tools or discipline. It&