Incident playbook: detect and triage third‑party outages before customers notice

Stop third‑party outages from becoming customer incidents: an SRE playbook

Hook: You woke up to pagers: CDN 5xx spikes, an auth provider timing out, and half your API error budget burned. Customers complain before your dashboard paints the full picture. In 2026, with more distributed dependencies and AI‑driven tooling, detectable failure modes are faster and subtler — but preventable. This playbook shows exactly how to detect and triage third‑party outages before customers notice using synthetic monitoring, dependency mapping, tuned alerting, and automated rollback/traffic routing to minimize blast radius.

Why third‑party outages deserve a dedicated SRE playbook in 2026

Third‑party services — CDNs, SaaS auth, payment gateways, logging/export pipelines, and cloud managed databases — are now deeply embedded into developer workflows. Two trends that shaped 2025 and continue into 2026 make third‑party incidents more critical:

• Higher service coupling: Microservices and serverless adoption increased interdependence. A degraded OIDC provider can cascade into API errors across regions.
• Smarter observability: OpenTelemetry is the universal telemetry substrate, and AI anomaly detection and edge AI assistants shipped broadly in late 2025 — giving SREs earlier signals but also more noisy hypotheses if not tuned.

That means the SRE playbook must focus on early, precise detection and surgical mitigation: reduce blast radius, preserve critical user journeys, and avoid overreacting to noisy signals.

Playbook overview — what success looks like

High‑level flow: detect → triage → mitigate → rollback/route → learn. Below is a concise checklist that we'll expand into actionable steps.

1. Deploy targeted synthetic tests for critical user journeys and third‑party touchpoints.
2. Maintain a live dependency map linked to SLOs and synthetic tests.
3. Use evidence‑weighted alerting thresholds (synthetic + telemetry + error budget).
4. Automate traffic routing or rollback with staged policies to limit blast radius.
5. Run a fast triage runbook, then perform RCA and cost/observability improvements.

Synthetic monitoring: detect failures that matter

Synthetic monitoring should replicate the smallest set of steps that prove your product works: auth, core API call, and a payment or upload if relevant. In 2026, synthetic tests should be:

• Low‑latency and edge-distributed (run from multiple regions/PoPs).
• Short, deterministic, and instrumented (trace propagation).
• Adaptive in frequency — increase run rate during a detected anomaly.

Practical config (curl test as a baseline):

# basic synthetic check (run by cron/runner/edge function)
curl -sS -w '\n%{http_code} %{time_total}\n' -o /dev/null \
  -H 'X-Synthetic: true' \
  'https://api.example.com/v1/health?test=auth' --max-time 5

Better: instrument synthetic checks with OpenTelemetry headers so traces correlate with internal traces. Example pseudocode:

// pseudo: HTTP call that injects OT headers
injectTraceContext(request)
request.addHeader('X-Synthetic', 'true')
response = httpClient.do(request)
emitSpan('synthetic.check', attrs={ 'target': '/v1/health', 'code': response.status })

Frequency guidance: aim for 15s–60s for critical paths in production, 1–5 min for less critical. To control cost, use adaptive scaling: default 60s, increase to 5–15s when an anomaly is suspected.

Dependency mapping: map impact to owners and SLOs

Dependency mapping turns a sea of signals into an answer to “who owns this?” and “how bad is it?” Modern tools make this data dynamic; build a live service graph linked to SLOs and synthetic tests.

Components to maintain in the map:

• Service node (name, owner, contact, SLOs)
• Third‑party node (provider, dependency type, SLA, cost impact)
• Edges showing request flow and failure propagation paths

Example (DOT) snippet for a small graph:

digraph service_graph {
  web -> api [label="HTTPS /v1/*"]
  api -> auth_provider [label="OIDC token"]
  api -> payments [label="POST /charge"]
}

Operationalize the map:

• Embed dependency metadata in your CMDB or service catalog (owner, priority).
• Attach synthetic tests and SLOs to graph nodes. When a synthetic test fails, the UI should highlight the node and its upstream/downstream scope.
• Use the graph at triage to quickly identify collateral risk and which teams to page.

Alerting: evidence‑weighted thresholds and noise control

By 2026 many teams use AI‑assisted anomaly detection. That helps, but you must still control false positives. The best approach combines multiple evidence sources:

1. Synthetic failure count and rate (regionally segmented)
2. Real user errors rate (5xx, key transaction latency)
3. SLO burn rate over a short window

Example Prometheus-style alert rule (conceptual):

groups:
- name: third_party_alerts
  rules:
  - alert: ThirdPartyAuth_Degraded
    expr: |
      (increase(synthetic_auth_failures_total[5m]) > 3 and
       increase(http_requests_total{job="api",status=~"5.."}[5m]) > 50) or
      (slo_burn_rate_auth > 2)
    for: 2m
    labels:
      severity: page
    annotations:
      summary: "Auth provider degraded in region {{ $labels.region }}"

Key rules:

• Don't page on single noisy signals. Require correlated signals across synthetic checks and user telemetry.
• Use dynamic thresholds. For example, base alerting on burn rate and not raw latency when traffic is variable.
• Region-aware policies. Page only if the issue is cross‑regional or impacting >= X% of traffic for a region.

Triage: runbook and fast decision tree

When an alert fires, follow a concise triage decision tree. Keep the first phase focused on containment and proof.

Phase 0 — quick verification (0–3 minutes)

• Check synthetic test details: timestamps, regions, error codes.
• Correlate with service graph to see which third parties are implicated.
• Confirm whether error budget is being consumed and at what burn rate.
• Post to incident channel with minimal fields: impact, suspected dependency, regions, immediate mitigation (if any).

Phase 1 — scope and prioritize (3–10 minutes)

• Identify critical user journeys at risk (using synthetic + SLO mapping).
• Decide: mitigate with routing/rollback, throttle, or wait (if small impact).
• Contact the third‑party provider if SLA indicates an outage or degradation.

Phase 2 — contain (10–30 minutes)

• Apply traffic routing or partial rollbacks (examples next).
• Enable defensive rate limits and circuit breakers for affected calls.
• Scale up resilient components (e.g., increase local caches or fallback caches).

Automated rollback and traffic routing — minimize blast radius

Automation reduces time to mitigation. Two common strategies:

• Traffic routing: Shift users away from failing paths or regions (weighted routing, canary rollbacks, cloud load balancer rules).
• Automated rollback: Revert the latest deployment that introduced a dependency change, with safety checks.

Examples:

Istio/Envoy + Flagger canary rollback

apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: api
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api
  canaryAnalysis:
    interval: 1m
    threshold: 5
    metrics:
    - name: request-success-rate
      threshold: 99
    - name: synthetic-checks
      threshold: 100

Flagger can automatically rollback if custom metrics (including synthetic) breach thresholds. For deployment and rollback patterns, see our DevOps playbook with examples and safety gates.

Cloud DNS/ALB weighted failover

For non‑Kubernetes stacks, use weighted DNS or ALB target group weights to steer traffic away from regions that show degraded synthetic checks. Example flow:

1. Detect region R failing synthetic checks.
2. Reduce weight for R to 0.1 (10%) on ALB/Route53.
3. Monitor for recovery; progressively restore weight.

Key safety rules:

• Limit rate of automated weight change to avoid thrashing.
• Require multi-signal confirmation before automated routing changes.
• Log every automated action and link it in the incident timeline.

Minimize blast radius with architectural patterns

Use these patterns to make mitigations safe and effective:

• Bulkheads: isolate resources for critical flows so one failing integration doesn't exhaust system capacity.
• Circuit breakers: fail fast when a dependency degrades and fall back to cached or degraded experiences.
• Graceful degradation: serve limited functionality (e.g., view only) rather than full failure.

Implementing these patterns buys time to triage and gives automated runbooks meaningful options (throttle vs rollback vs route).

Observability + cost optimization: balance signal quality with expense

High fidelity monitoring is expensive. Optimize for cost without sacrificing early detection:

• Tier synthetic checks: critical journeys run at higher frequency; noncritical ones run less often.
• Use sampling and tail‑latency tracing. In 2026 eBPF‑backed capture lets you collect high‑resolution metrics only when an anomaly is suspected (see on-device capture and live transport patterns).
• Use adaptive retention: keep high-granularity data during incidents and downsample afterwards.

Example policy:

During normal operation: 1m synthetic checks for core flows. On suspected degradation: switch to 15s, enable full tracing for affected service for 30 minutes.

Post‑incident: learn fast and harden

After containment, run a short blameless postmortem focused on three outcomes:

1. Prevent recurrence by improving dependency SLAs, synthetic coverage, or adding bulkheads.
2. Automate the successful mitigation path (codify manual steps into playbooks and pipelines).
3. Optimize monitoring costs with better tiering and adaptive capture windows.

Metrics to track postmortem:

• Mean time to detect (MTTD) and to mitigate (MTTM) for third‑party incidents.
• Number of customers affected and SLO burn impact.
• Cost of synthetic monitoring and observability during the incident.

Actionable runbook snippets and checklist

Pin these snippets into your incident channel and runbooks.

Incident channel template

INCIDENT: ThirdPartyAuth_Degraded
Impact: Regions: eu-west, us-east; auth failures 20% of logins
Suspected: auth-provider.example.com
Actions:
- [ ] Verify synthetic failures (owner: @sre)
- [ ] Enable degraded login path (owner: @api-team)
- [ ] Route 90% traffic to other regions (owner: @infra)
- [ ] Contact vendor (owner: @vendor-relations)

Quick rollback script (GitOps example)

# pseudo-script: revert to previous k8s image tag using kubectl or Flux/GitOps
kubectl set image deployment/api api=myrepo/api:stable-20260110 --record
# or open PR to revert deployment in GitOps repo and merge

Checklist to minimize blast radius (one‑pager):

• Do you have synthetic tests for every critical third‑party path?
• Is the dependency map current and linked to SLOs/owners?
• Are alerts requiring multi‑signal confirmation configured?
• Are routing/rollback automations gated and auditable?
• Do you measure MTTD/MTTM and cost of observability?

2026 trends to bake into your SRE playbook

• OpenTelemetry everywhere: treat traces as first‑class for synthetic tests. Correlate synthetic spans with user traces to reduce noise.
• AI‑assisted triage: use generative assist to summarize incident timelines, but validate AI suggestions with evidence rules. See Edge AI Code Assistants and how they change triage workflows.
• eBPF on demand: use kernel‑level capture selectively for unexplained tail latency instead of full tracing all the time; pair on-demand capture with affordable OLAP storage like ClickHouse-style systems to retain key traces.
• Policy as code: codify routing/rollback gates in pipelines so automated actions are auditable and reversible. Our micro-apps and DevOps playbook has examples for gating automation safely.

Final takeaways: make third‑party outages survivable

In 2026, third‑party outages are not a question of if but when. The difference between a noisy incident and a customer outage is a playbook that combines synthetic monitoring, a live dependency map, evidence‑weighted alerting, and controlled traffic routing/rollback. Automate what works, require multi‑signal confirmation, and always aim to limit the blast radius while preserving critical user journeys.

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