How many metrics should be in a playbook?

Keep it to 5–7 leading indicators per service. If a responder can’t scan them in under 30 seconds, you’ve overfit. Burn rate (fast/slow), tail latency slope, one or two saturation signals, and one queue/DB early warning cover most cases.

Do we need Argo Rollouts, or will feature flags suffice?

Use both. Feature flags are surgical, but they won’t help if the deployment itself is toxic (memory leak, config drift). Rollouts gives you progressive delivery and auto-rollback based on metrics; flags give you instant kill for specific code paths.

We’re still on VMs and a legacy stack. Is this overkill?

No. The principles hold. You can wire Prometheus/OpenTelemetry to VM services, use Terraform for standardization, and script rollbacks with bash + systemd. GitOps is about versioned desired state, not just Kubernetes.

How do we prevent AI-generated regressions from slipping through?

Require provisional SLOs for new endpoints, add analysis templates to staging canaries, and monitor p99 slope post-merge for 24–48 hours. If signals trip, auto-disable the feature flag. Treat AI assistants as junior devs who need guardrails.

What KPIs should leadership track to know this is working?

MTTR, change failure rate, pages per week per team, and error budget spent per journey. If those trend down and on-call satisfaction goes up, your playbooks and automation are paying off.

Reliability-observability · Nov 24, 2025 · 10 minute read

Stop Paging on Vanity Metrics: Playbooks That Predict and Auto-Roll Back Before Users Notice

War-tested incident playbooks that scale across teams by wiring leading indicators to triage and GitOps rollbacks.

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Stop Paging on Vanity Metrics: Playbooks That Predict and Auto-Roll Back Before Users Notice

Key takeaways

Implementation checklist