| name | Alerts |
| description | Smart alerting patterns for AI agents - deduplication, routing, escalation, and fatigue prevention |
Alert Fatigue Prevention
Group alerts by root cause, never by individual symptoms.
Use labels: alertname, service, cluster - not instance IDs.
group_by: ['alertname', 'service']
Implement severity hierarchy: P0 (pages immediately) > P1 (within 15min) > P2 (business hours) > P3 (weekly review).
P0: Service completely down, data loss, security breach.
P1: Degraded performance, partial outage, high error rates.
Set cooldown periods to prevent alert spam.
Minimum 5 minutes between identical alerts, 30 minutes for cost alerts.
repeat_interval: 5m
repeat_interval: 30m
Use inhibition rules to suppress symptoms when root cause fires.
If "Database Unreachable" fires, silence all "API High Latency" alerts from same cluster.
AI Agent Monitoring Patterns
Monitor token/API usage with exponential alerting thresholds.
Alert at 2x, 5x, 10x normal usage - costs can spiral quickly.
Track: tokens per minute, cost per request, API rate limits approached.
Set behavioral drift alerts on response quality degradation.
Compare current outputs to baseline with sample prompts every hour.
Alert when success rate drops below 85% or response time exceeds 2x baseline.
Monitor for infinite loops in multi-agent workflows.
Alert if same prompt sent >3 times in 5 minutes or agent hasn't responded in 10 minutes.
Include correlation IDs to trace conversation chains.
Track silent failures through downstream metrics.
Monitor: tasks completed vs started, user satisfaction scores, retry attempts.
These catch errors that don't throw exceptions.
Routing and Escalation Rules
Route by expertise domain, not arbitrary on-call schedules.
Database alerts → DB team, API alerts → backend team, cost alerts → platform team.
Only escalate to managers for P0 incidents lasting >30 minutes.
Use progressive escalation with increasing urgency.
P1 alerts: Slack notification → 5min wait → SMS → 10min wait → phone call.
Include runbook links in every alert for faster resolution.
Set context-aware routing based on time and impact.
Business hours: Route to primary team. Off-hours: Route to on-call only for P0/P1.
If >100 users affected: Immediately escalate regardless of severity.
Webhook Reliability Patterns
Always include correlation IDs for alert lifecycle management.
Generate UUID for each incident, use it to create/update/resolve alerts.
Essential for bi-directional integrations with PagerDuty/Slack.
Implement exponential backoff for webhook failures.
Retry after 1s, 2s, 4s, 8s, 16s, then mark failed and escalate.
Log webhook response codes/times for debugging delivery issues.
Use webhook verification to prevent spoofing.
Validate signatures using HMAC-SHA256 with shared secret.
Always check timestamp to prevent replay attacks (max 5 min old).
Implement circuit breaker pattern for unreliable endpoints.
After 5 consecutive failures, mark endpoint down and use backup channel.
Re-test every 30 seconds until recovery confirmed.
Status Page Integration
Update status page automatically when P0/P1 alerts fire.
Create incident, post initial assessment within 5 minutes.
Include ETA and workaround if available.
Use component-based status updates matching your alert groups.
Map alert labels to status page components (API, Database, Auth, etc.).
Partial outages should show "Degraded Performance", not "Operational".
Runbook Automation
Embed runbook links directly in alert messages.
Format: "Alert: High CPU on web-01. Runbook: https://wiki/runbooks/high-cpu-web"
Links must be accessible from mobile devices for on-call engineers.
Trigger automated remediation for known issues.
Auto-restart stuck services, clear full disks, reset rate limits.
Always require human approval for destructive actions (scaling down, deleting data).
Log all automated actions taken in response to alerts.
Include: timestamp, action, result, approval chain.
Essential for post-incident reviews and compliance audits.
