Reliability Recovery After Incidents
Use C2O to recover from incident spikes and error-budget burn by clarifying outcomes, decision rights, and guardrails across the lifecycle.
Use case – Ops / Reliability Lead
Outcome
Error-budget burn returns to policy, pages per shift drop to a sustainable level, and incident decisions move from anecdote to thresholds.
Step 1 of the 3-step plan is to clarify the outcome using the Outcome Definition Worksheet for this scenario, then use the templates and playbooks below to run it.
Signals that matter
Each signal is backed by the metrics dictionary and the internal platform enablement case study.
Error budget burn
Error-budget burn brought back into policy
Decision latency
Decision latency on incident actions
Pages per shift (on-call)
Pages per shift target enforced
Mean time to restore (MTTR)
Mean time to restore (MTTR) improvement
Run this now
Move from reading to action with a small starter stack: one outcome worksheet, one contribution map, and two lifecycle playbooks.
Outcome Definition Worksheet (Reliability)
Frame reliability recovery in outcomes and guardrails, not just incident counts.
Contribution Mapping Canvas (Incidents)
Map Drive/Contribute/Enable/Advise/Inform for incident response and reliability decisions.
Run: Reliability & Error Budgets Playbook
Shape on-call posture, SLO policies, and error-budget guardrails for incident-heavy services.
Decide: Incident & Risk Decisions
Use decision ladders and thresholds to drive incident-related decisions without relitigation.
Decision rights
Decision Ladders for Reliability
Clarify who Drives incident decisions, when to escalate, and which thresholds gate shipping or rollback.
View Decision Rights hubCase proof
Before/after metrics and decision records from an internal platform enablement initiative.
Reliability Recovery After Incident Spikes
A cross-functional team used C2O to recover from incident spikes while keeping SLOs and on-call health within policy.
| Metric | Before | After |
|---|---|---|
| Error budget burn | 22% burned per 7 days at peak | Evidence |
| Incident rate | Frequent user-impacting incidents with unclear triggers | Evidence |