SLA Monitor Skill#

Purpose#

Help teams set up production-grade monitoring for AI agents and automated services. Covers uptime tracking, response time SLAs, error budgets, and incident escalation.

When to Use#

• Deploying AI agents to production
• Setting up monitoring for client-facing automation
• Creating SLA documentation for service agreements
• Building incident response procedures

Monitoring Stack Options#

Option 1: UptimeRobot (Free tier available)#

• 50 monitors free, 5-minute intervals
• HTTP, keyword, ping, port monitors
• Email + Slack + webhook alerts

Option 2: Better Stack (Formerly Uptime.com)#

• Status pages included
• Incident management built-in
• Free tier: 10 monitors

Option 3: Self-Hosted (Uptime Kuma)#

bash

docker run -d --restart=always -p 3001:3001 -v uptime-kuma:/app/data --name uptime-kuma louislam/uptime-kuma:1

SLA Tiers#

Tier 1: Standard ($1,500/mo)#

• 99.5% uptime guarantee (43.8h downtime/year)
• Response within 4 hours (business hours)
• Monthly performance report

Tier 2: Professional ($3,000/mo)#

• 99.9% uptime guarantee (8.76h downtime/year)
• Response within 1 hour (business hours)
• Weekly performance reports
• Quarterly optimization reviews

Tier 3: Enterprise ($5,000+/mo)#

• 99.95% uptime (4.38h downtime/year)
• Response within 15 minutes (24/7)
• Real-time dashboard access
• Dedicated support channel

Alert Configuration Template#

yaml

monitors:
  - name: "Agent Health Check"
    type: http
    url: "https://your-agent-endpoint/health"
    interval: 300  # 5 minutes
    alerts:
      - type: email
        threshold: 1  # alert after 1 failure
      - type: slack
        webhook: "${SLACK_WEBHOOK}"
        threshold: 2  # alert after 2 consecutive failures
      - type: sms
        threshold: 3  # escalate after 3 failures

  - name: "API Response Time"
    type: http
    url: "https://your-agent-endpoint/api"
    interval: 60
    expected_response_time: 2000  # ms
    alerts:
      - type: slack
        condition: "response_time > 5000"

error_budget:
  monthly_target: 99.9
  burn_rate_alert: 2.0  # Alert if burning 2x normal rate

Incident Response Playbook#

Severity 1 — Total Outage#

1. Acknowledge within 5 minutes
2. Status page update within 10 minutes
3. Root cause identification within 30 minutes
4. Resolution or workaround within 2 hours
5. Post-mortem within 24 hours

Severity 2 — Degraded Performance#

1. Acknowledge within 15 minutes
2. Investigation within 30 minutes
3. Resolution within 4 hours
4. Summary report within 48 hours

Severity 3 — Minor Issue#

1. Acknowledge within 1 hour
2. Resolution within 24 hours
3. Logged for next review cycle

Error Budget Calculator#

text

Monthly minutes: 43,200 (30 days)
99.9% SLA = 43.2 minutes downtime allowed
99.5% SLA = 216 minutes downtime allowed
99.0% SLA = 432 minutes downtime allowed

Burn rate = (actual downtime / budget) × 100
If burn rate > 50% with 2+ weeks remaining → review needed
If burn rate > 80% → freeze deployments

Status Page Template#

Provide clients with a public status page showing:

• Current system status (operational / degraded / outage)
• Component-level status (Agent A, Agent B, API, Dashboard)
• Uptime percentage (30-day rolling)
• Incident history with resolution notes
• Scheduled maintenance windows

Next Steps#

Need managed AI agents with built-in SLA monitoring? → AfrexAI handles deployment, monitoring, and maintenance for $1,500/mo → Book a call: https://calendly.com/cbeckford-afrexai/discovery-call → Learn more: https://afrexai-cto.github.io/aaas/landing.html