Observability Primer for Delivery and Platform Teams
Modern delivery performance depends on fast feedback loops. Observability gives you those loops by turning runtime behavior into actionable signals.
The four signal types
- Metrics: numeric time-series for trend and threshold monitoring.
- Logs: event detail for debugging and incident timelines.
- Traces: request-level path and latency breakdown across services.
- Alerts: routing logic that tells the right team when thresholds or conditions fail.
What to stand up first
- A metrics dashboard for platform health.
- Log exploration with service-level filters.
- Alert rules tied to user-facing symptoms.
- A short incident runbook for top failure modes.
This follows the same progression seen in uFawkes observability docs: get metrics and logs reliable first, then expand into trace instrumentation for deeper diagnostics.
Common implementation gaps
- Tracing backend is running, but apps emit no spans.
- Dashboards exist, but queries do not match available metric names.
- Alerts trigger, but no runbook owner is defined.
- Data is present, but not connected to DORA review cadences.
Connect observability to delivery outcomes
Use weekly metric reviews to answer:
- Which pipeline stage is extending lead time?
- Which services drive change failures?
- How fast does the team restore production health?
Then close the loop with the DORA primer and capability planning in the AI capabilities guide.
Run this yourself: GitHub repo link