ai-sre-incident-response skill

AI SRE Incident Response

Apply SRE rigor to AI systems where incidents include quality regressions, unsafe outputs, and budget explosions.

When to Use This Skill

• An LLM endpoint begins returning degraded or hallucinated answers
• Token spend spikes beyond budget thresholds
• A model provider goes down and traffic must fail over
• Safety guardrails fire at abnormal rates
• A new model deployment causes latency or accuracy regression

Prerequisites

• Prometheus and Alertmanager deployed with scrape targets for AI services
• Grafana dashboards for golden signals (latency, error rate, cost, quality)
• On-call rotation configured in PagerDuty, Opsgenie, or equivalent
• Runbook repository accessible to responders
• Rollback mechanism for model and prompt versions (GitOps or feature flags)

AI Incident Classes

• Availability incident: model/provider unavailable, timeout storm.
• Quality incident: answer accuracy or tool success drops below SLO.
• Safety incident: harmful or policy-violating outputs increase.
• Cost incident: unexpected token or provider spend spike.

Severity Framework

Golden Signals for AI Services

• Request success rate
• Latency (queue + generation + tool execution)
• Hallucination/groundedness proxy metrics
• Cost per minute and per tenant
• Guardrail violation rate

Prometheus Alert Rules

# prometheus-ai-alerts.yaml
groups:
  - name: ai-service-alerts
    rules:
      - alert: ModelEndpointDown
        expr: up{job="llm-inference"} == 0
        for: 2m
        labels:
          severity: sev1
        annotations:
          summary: "LLM inference endpoint {{ $labels.instance }} is down"
          runbook_url: "https://runbooks.internal/ai/model-outage"

      - alert: HighHallucinationRate
        expr: |
          rate(llm_hallucination_detected_total[10m])
          / rate(llm_requests_total[10m]) > 0.15
        for: 5m
        labels:
          severity: sev2
        annotations:
          summary: "Hallucination rate above 15% for {{ $labels.model }}"
          runbook_url: "https://runbooks.internal/ai/quality-regression"

      - alert: TokenCostExplosion
        expr: |
          sum(rate(llm_token_cost_dollars[5m])) by (tenant)
          > 0.50
        for: 3m
        labels:
          severity: sev2
        annotations:
          summary: "Token spend exceeds $0.50/min for tenant {{ $labels.tenant }}"
          runbook_url: "https://runbooks.internal/ai/cost-spike"

      - alert: LatencyP95Exceeded
        expr: |
          histogram_quantile(0.95,
       

Response Playbooks

Model Outage Runbook

TRIGGER: ModelEndpointDown fires for > 2 minutes
RESPONDER: On-call AI platform engineer

1. Acknowledge alert in PagerDuty.
2. Check provider status page (e.g., status.openai.com).
3. Verify network connectivity:
     curl -s -o /dev/null -w "%{http_code}" https://api.provider.com/health
4. If provider is down:
     a. Enable fallback model route in gateway config.
     b. kubectl set env deployment/llm-gateway FALLBACK_ENABLED=true
     c. Verify fallback traffic is flowing via Grafana dashboard.
5. If self-hosted model is down:
     a. Check pod status: kubectl get pods -l app=llm-inference -n ai
     b. Check GPU health: kubectl logs -l app=llm-inference --tail=50
     c. Restart if OOM: kubectl rollout restart deployment/llm-inference -n ai
6. Freeze all deployments:
     kubectl annotate deployment --all deploy-freeze=true -n ai
7. Communicate ETA in #incident-channel.
8. When resolved, unfreeze and run smoke tests.

Quality Regression Runbook (Hallucination Spike)

TRIGGER: HighHallucinationRate or ModelQualityDrop fires
RESPONDER: On-call AI engineer + ML lead

1. Acknowledge alert. Open incident ticket.
2. Identify scope:
     - Which model version? Check deployment metadata.
     - Which routes/tenants affected? Filter by labels in Grafana.
3. Check recent changes:
     - Model version promotion in last 24h?
     - Prompt template changes in last 24h?
     - Retrieval index rebuild in last 24h?
4. If recent model change:
     kubectl rollout undo deployment/llm-inference -n ai
5. If recent prompt change:
     git revert <commit> && git push  # triggers GitOps redeploy
6. Increase trace sampling to 100% for affected route:
     kubectl set env deployment/llm-gateway TRACE_SAMPLE_RATE=1.0
7. Run offline eval suite against current production:
     python run_evals.py --target prod --suite quality --compare baseline
8. Confirm metrics return to baseline before closing.

Token Cost Explosion Runbook

TRIGGER: TokenCostExplosion fires
RESPONDER: On-call platform engineer

1. Identify top consumers:
     Query: topk(10, sum(rate(llm_token_cost_dollars[15m])) by (tenant, model, route))
2. Check for runaway loops:
     - Agent retry storms (exponential token growth per request)
     - Missing max_tokens caps on new routes
     - Cache bypass due to config change
3. Apply immediate caps:
     kubectl patch configmap llm-quotas -n ai --patch '
       data:
         max_tokens_per_request: "4096"
         rpm_limit: "60"
     '
4. Enable semantic cache if disabled:
     kubectl set env deployment/llm-gateway CACHE_ENABLED=true
5. Route traffic to cheaper model tier:
     kubectl set env deployment/llm-gateway DEFAULT_MODEL=gpt-4o-mini
6. Notify affected tenants of temporary limits.
7. Open postmortem with cost attribution analysis.

Escalation Procedures

Level 1 (0-15 min):  On-call AI platform engineer
Level 2 (15-30 min): AI platform team lead + affected product owner
Level 3 (30-60 min): Engineering director + security (if safety incident)
Level 4 (60+ min):   VP Engineering + legal (if compliance/data incident)

Safety incidents always start at Level 2 minimum.
Provider-side incidents: open support ticket immediately at Level 1.

Detection Queries (PromQL)

# Request success rate by model
1 - (
  sum(rate(llm_requests_total{status="error"}[5m])) by (model)
  / sum(rate(llm_requests_total[5m])) by (model)
)

# Cost per successful answer
sum(rate(llm_token_cost_dollars[5m])) by (route)
/ sum(rate(llm_requests_total{status="success"}[5m])) by (route)

# Hallucination rate trend (1h window, 5m steps)
rate(llm_hallucination_detected_total[1h])
/ rate(llm_requests_total[1h])

# Latency breakdown by stage
histogram_quantile(0.95, rate(llm_retrieval_duration_seconds_bucket[5m]))
histogram_quantile(0.95, rate(llm_generation_duration_seconds_bucket[5m]))
histogram_quantile(0.95, rate(llm_tool_execution_duration_seconds_bucket[5m]))

# Tenant cost leaderboard
topk(10, sum(rate(llm_token_cost_dollars[1h])) by (tenant))

Postmortem Requirements

• Timeline with detector and responder timestamps
• Blast radius by tenant and feature
• Missed signals and alert tuning actions
• Concrete hardening tasks with owners and due dates
• Cost impact (dollars, tokens, affected requests)
• Customer communication log

Postmortem Template

## Incident Summary
- **Severity**: SEVx
- **Duration**: start_time - end_time (Xh Ym)
- **Detection**: How was it detected? (alert / customer report / manual)
- **Impact**: X tenants, Y requests, $Z cost

## Timeline
| Time (UTC) | Event |
|------------|-------|
| HH:MM | Alert fired |
| HH:MM | Responder acknowledged |
| HH:MM | Root cause identified |
| HH:MM | Mitigation applied |
| HH:MM | Incident resolved |

## Root Cause
[Description]

## Action Items
| Action | Owner | Due Date | Status |
|--------|-------|----------|--------|
| Tune alert threshold | @engineer | YYYY-MM-DD | Open |
| Add fallback route | @platform | YYYY-MM-DD | Open |

Chaos Engineering for AI Systems

Regularly test incident readiness:

• Provider failover drill: block provider API at network level, verify fallback activates within SLO.
• Model rollback drill: deploy known-bad model version, verify automated quality gate catches it.
• Cost cap drill: simulate runaway token usage, verify quotas trigger before budget threshold.
• Cache failure drill: disable semantic cache, verify system degrades gracefully.

Troubleshooting

Related Skills

• incident-response (incident-response) - Standard incident process and evidence
• alerting-oncall (alerting-oncall) - Paging and escalation policy
• llm-cost-optimization (llm-cost-optimization) - Spend controls and efficiency patterns
• agent-observability (agent-observability) - Instrument requests, traces, and costs
• rag-observability-evals (rag-observability-evals) - RAG quality monitoring

Limitations

• Guidance executes against real environments: confirm target, blast radius, and rollback plan before applying anything.
• Never deploy to production without explicit approval. Docs-only import: upstream scripts and templates not bundled.

Example

git status && git diff --stat
kubectl diff -f manifest.yaml

Adapted from BagelHole/DevOps-Security-Agent-Skills (MIT); frontmatter, When to Use/Limitations, and safety boundaries added for upstream compliance. Docs-only import: helper scripts and templates not bundled.