AI SRE Best Practices: Scaling from 10 to 10,000 Incidents

I've watched teams implement AI SRE. The ones that succeed follow a playbook. The ones that fail ignore it and wonder why their $200k platform sits unused.

This isn't theory. These are battle-tested best practices from teams running AI SRE at massive scale: 10,000+ incidents/month, 500+ microservices, 24/7 production loads. If you're implementing AI SRE, this is your survival guide.

1. Start Small, Scale Fast (The 80/20 Rule)

The #1 mistake: trying to automate everything on day one. Instead, apply the 80/20 rule.

SELECT 
  incident_type, 
  COUNT(*) as frequency,
  AVG(mttr_minutes) as avg_resolution_time
FROM incidents
WHERE created_at > NOW() - INTERVAL '90 days'
GROUP BY incident_type
ORDER BY frequency DESC
LIMIT 5;

2. Build the Runbook Library First

AI SRE is only as good as your runbooks. If your runbooks are tribal knowledge ("ask Sarah, she knows"), AI can't help.

# Runbook: [SERVICE_NAME] - [INCIDENT_TYPE]

## 1. Symptoms (How do I know this is happening?)
- Alert: [ALERT_NAME]
- Error message: [EXACT ERROR TEXT]
- Metrics: [e.g., "CPU >90% for 5 min"]

## 2. Diagnosis (How do I confirm?)
```bash
kubectl logs -n prod [SERVICE_NAME] --tail=100 | grep "ERROR"
curl https://[SERVICE_NAME]/health
```

## 3. Resolution (Step-by-step fix)
```bash
# Step 1: Restart pod
kubectl rollout restart deployment/[SERVICE_NAME] -n prod

# Step 2: Verify health
kubectl get pods -n prod | grep [SERVICE_NAME]

# Step 3: Monitor for 5 min
watch -n 10 'curl -s https://[SERVICE_NAME]/health'
```

## 4. Rollback Plan (If fix doesn't work)
```bash
kubectl rollout undo deployment/[SERVICE_NAME] -n prod
```

## 5. Prevention (How do we stop this recurring?)
- Root cause: [e.g., "Memory leak in user-session cache"]
- Long-term fix: [e.g., "Add TTL to cache, increase memory limits"]
- Owner: [TEAM/PERSON]
- Due: [DATE]

## 6. AI Training Data
- Confidence: 95% (works in 19/20 incidents)
- Blast radius: Low (single service)
- Reversibility: Instant (rollback in 30s)
- Auto-approve: YES (after 30-day shadow mode)

3. Measure Everything (The Metrics That Matter)

"You can't improve what you don't measure." Here are the 7 metrics every AI SRE team should track:

4. The "Shadow Mode" Phase is Non-Negotiable

Teams that skip shadow mode regret it. Shadow mode = AI watches but doesn't act. Here's why it matters:

5. Tag Services by Risk Level

Not all services are equal. Payment processing ≠ internal analytics dashboard. Use risk tags.

# Kubernetes labels for risk tagging
apiVersion: v1
kind: Service
metadata:
  name: payment-service
  labels:
    app: payment
    risk-level: "critical"  # AI will always request approval
    compliance: "pci-dss"   # Extra audit trail required

---

apiVersion: v1
kind: Service
metadata:
  name: analytics-worker
  labels:
    app: analytics
    risk-level: "low"       # AI can auto-remediate
    compliance: "none"

6. The "One Bad Fix" Rule

If AI makes production worse even once, your team will lose trust. Here's how to prevent it:

if (error_rate_after_fix > error_rate_before_fix):
    # AI made it worse!
    rollback_last_action()
    alert_human("AI fix backfired - rolled back")
    disable_auto_remediation_for_this_pattern()
    # Require human investigation before re-enabling

7. Celebrate Wins, Learn from Failures

AI SRE adoption is a culture shift. Treat it like one.