What’s the quickest win to reduce MTTD without rebuilding our observability stack?

Add SLO burn-rate alerts for your top customer journey and stamp `git_sha`/`service.version` into telemetry so every page is deployment-aware. You’ll cut detection time and triage time without changing vendors.

Should we page on CPU or memory at all?

Only when it’s a proven leading indicator for your failure modes (e.g., CPU throttling causing tail latency, memory pressure causing OOMKills). Page on saturation symptoms tied to impact, not raw utilization.

How do we prevent canary gates from blocking deploys due to noise?

Use short analysis windows with `failureLimit`, require consecutive failures, and gate on burn-rate/tail latency rather than single spikes. Multi-window logic and sane baselines reduce flapping.

What if our rollback isn’t safe?

Then don’t automate rollback first—automate a pause/abort and a feature-flag-off path. Fix rollback safety as a separate reliability investment (DB migrations, backward compatibility, data shape changes).

Does this work for monoliths, or only Kubernetes microservices?

It works for both. The mechanics change (your “rollout gate” might be a Jenkins stage instead of Argo Rollouts), but burn-rate + leading indicators + deployment-aware alerts applies everywhere.

Reliability-observability · Dec 19, 2025 · 8 minute read

Your Dashboards Aren’t Detecting Incidents — Your Rollouts Are

Automated incident detection that cuts MTTD comes from leading indicators: SLO burn-rate, saturation, queue growth, and rollout regressions. Wire them into triage and deployment automation so you’re not “discovering” outages in Slack.

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Your Dashboards Aren’t Detecting Incidents — Your Rollouts Are

Key takeaways

Implementation checklist