Intent
Design logging and metrics before automation so failures are explainable.
When to use
- Reliability and trust are critical to adoption.
- Systems must be tuned based on real usage and failures.
- Stakeholders need confidence in data and operations.
- You want continuous improvement cycles.
Core mechanics
- Instrument critical paths and external dependencies.
- Define signals before building automation.
- Create dashboards and feedback loops.
- Review signals and adjust the system regularly.
Implementation checklist
- Identify the top signals that reflect success or failure.
- Add structured logging with correlation IDs.
- Define metrics, thresholds, and alert rules.
- Build dashboards for operators and stakeholders.
- Set review cadence for signals and incidents.
- Feed learnings into backlog and design updates.
Failure modes and mitigations
- Too much noise -> refine metrics and reduce verbosity.
- Missing context -> add correlation IDs and metadata.
- Unowned dashboards -> assign an owner and review cadence.
- Alert fatigue -> tune thresholds and routes.
Observability and validation
- System health metrics and error budgets.
- Alert response times and acknowledgment rates.
- Dashboard usage and coverage.
- Post-incident review notes.
Artifacts
- Dashboard and alert definitions.
- Log schema and example log lines.
- Incident or postmortem templates.