Observability Review Checklists and Templates

This appendix turns the guide into a working review kit. Use it when a team is designing a new service, auditing an existing observability stack, preparing for launch, or cleaning up alerting and telemetry drift after incidents.

The point is not to force every system into the same toolchain. The point is to make sure the same critical questions get asked every time: what signals matter, who owns them, how responders will use them, and what cost or governance limits apply.

    flowchart LR
	    A["Service or Platform Review"] --> B["Instrumentation"]
	    B --> C["Dashboards and SLOs"]
	    C --> D["Alerts and Response Paths"]
	    D --> E["Incident Feedback"]
	    E --> F["Schema, Cost, and Governance Updates"]

The review loop matters because observability quality decays when teams stop revisiting it after the first implementation.

How To Use These Templates

• Use the checklists during design reviews, not only after incidents.
• Treat incomplete answers as follow-up work, not as a reason to skip the review.
• Keep one version per service or platform capability so ownership stays visible over time.
• Adapt the examples to your tooling, but keep the decision points intact.

Service Launch Readiness Checklist

Use this before a new service, feature, or dependency goes live.

• Are the critical user journeys and failure-sensitive operations identified explicitly?
• Do logs, metrics, traces, and events each serve a clear purpose rather than duplicate each other?
• Is there a stable request or operation identifier that can connect evidence across signals?
• Are the main success, failure, latency, and saturation indicators instrumented?
• Are dashboards present for both fleet-level health and request-level investigation?
• Are paging alerts tied to user-visible symptoms or SLO burn rather than only low-level causes?
• Is ownership clear for telemetry schema, alert rules, dashboard upkeep, and post-incident fixes?
• Are retention, sampling, and cardinality limits explicit?
• Has sensitive data handling been reviewed for logs, labels, and trace attributes?
• Is there a defined runbook or first-response path for the most likely failure modes?

Instrumentation Review Template

Use this to document what a service emits and why.

 1service: checkout-api
 2owners:
 3  engineering_team: payments-platform
 4  oncall_rotation: checkout-primary
 5critical_user_journeys:
 6  - place_order
 7  - update_shipping_method
 8  - calculate_tax
 9signals:
10  logs:
11    purpose: capture request outcome, validation failures, and dependency errors
12    required_fields:
13      - timestamp
14      - service_name
15      - environment
16      - request_id
17      - tenant_id
18      - outcome
19  metrics:
20    purpose: track request rate, error rate, latency, and queue depth
21    key_indicators:
22      - http_server_requests_total
23      - http_server_request_duration_seconds
24      - dependency_failures_total
25  traces:
26    purpose: show latency contribution across internal and external spans
27  events:
28    purpose: record business-significant transitions such as order_submitted
29known_gaps:
30  - third_party_tax_provider does not yet emit span attributes for retry count

Dashboard Review Checklist

Use this when reviewing existing dashboards or building new ones.

• Does the dashboard serve one audience clearly: executive awareness, service ownership, on-call triage, or deep investigation?
• Can a responder move from top-level symptom to narrower evidence without opening ten unrelated pages?
• Are charts aligned to decisions, such as “is this impacting customers?” or “which dependency is regressing?”
• Are units, aggregation windows, and thresholds obvious?
• Are noisy or low-signal charts removed instead of accumulated forever?
• Does the dashboard show recent deploys, config changes, or incident annotations when they matter?
• Are drill-down paths documented so a new responder can follow them under pressure?

SLO Review Template

Use this when an objective needs to be justified or challenged.

 1service: search-api
 2user_journey: search_results
 3indicator:
 4  name: successful_search_requests
 5  measurement: percentage of searches returning a valid response within 800ms
 6objective:
 7  target: 99.5%
 8  window: 30d
 9error_budget:
10  policy: freeze risky launches if burn rate exceeds threshold for two consecutive review windows
11exclusions:
12  - internal load tests
13  - approved maintenance windows
14review_questions:
15  - Does this indicator reflect what users actually experience?
16  - Are known partial failures hidden by aggregation?
17  - What action changes when the budget is consumed faster than planned?

Alert Review Checklist

Use this to audit paging and notification quality.

• Does each paging alert map to an expected operator action?
• Is the alert driven by symptoms, burn rate, or a proven leading indicator rather than generic noise?
• Is the threshold based on real service behavior and not copied from another system?
• Does the alert include enough context to start triage without switching tools immediately?
• Is there a non-paging channel for lower-severity warnings and trend monitoring?
• Are suppressed, silenced, or auto-closed alerts reviewed for hidden risk?
• Are stale alerts removed when the service or architecture changes?

Incident Feedback Template

Use this after an incident to convert lessons into observability improvements.

 1incident_id: INC-2026-041
 2customer_impact:
 3  summary: checkout latency exceeded the objective for 27 minutes
 4failed_detection:
 5  - queue_lag rose 18 minutes before the page but was not routed to the owning team
 6missing_evidence:
 7  - retry count was absent from downstream tax span attributes
 8  - dashboard lacked deploy markers for the worker fleet
 9changes_required:
10  - add lag and retry indicators to the service dashboard
11  - route sustained queue lag to payments-platform on-call
12  - extend span schema to include downstream retry count
13owner: payments-platform
14review_due: 2026-04-10

Governance And Cost Review Checklist

Use this at the platform level.

• Are field names, label keys, and context conventions standardized across teams?
• Are high-cardinality labels or unbounded dimensions reviewed before rollout?
• Are retention tiers aligned to operational, forensic, and compliance use cases?
• Are trace sampling policies explicit for hot paths, cold paths, and incident overrides?
• Are access boundaries clear for customer-sensitive telemetry?
• Is one team clearly accountable for shared platform defaults and exceptions?

When A Review Is Good Enough

A review is strong when it leaves behind concrete decisions, owners, and follow-up work instead of generic approval language. If a team cannot answer who uses a signal, what action an alert should trigger, or what evidence will prove customer impact, the observability design is not finished yet.