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PagerDutyINC-3847
JD
API response times spiked to 15s, error rate at 23%. Users reporting timeouts. Need immediate investigation!
Analyzing system metrics
Datadog
>
Checking error logs
Sentry
>

Investigation Started

Analyzing system metrics and logs. Current hypotheses: network issues, database problems, or recent deployment.

Multiple potential causes identified.

Validating network health
Prometheus
>
Checking deployment history
GitHub
>

Testing initial hypotheses based on the symptoms:

Hypothesis 1: Network Issues

  • Check: Network latency measurement
  • Result: 12ms average (normal range)
  • Assessment: Network infrastructure appears healthy
  • Status: Ruled out

Hypothesis 2: Recent Deployment Impact

  • Check: Deployment history review
  • Result: Last release was 3 days ago
  • Assessment: No correlation with incident timeline
  • Status: Ruled out

Need to investigate other potential causes...

Analyzing DB connection pools
Datadog
>

Database Analysis

Found anomaly in connection pool utilization: 89% pool utilization (normal: 45%)

Requires Human Verification

Should I investigate auth-service memory patterns? This could impact active user sessions.

Yes, investigate auth-service but avoid session disruption. Show me what you find first.
Checking memory patterns
Kubernetes
>

Memory Leak Detected

auth-service memory: 2.1GB → 4.7GB (2h)

Problematic Code (auth-service/db.js:127):

javascript
GitHub
function createConnection() {
const connection = mysql.createConnection({
host: process.env.DB_HOST,
user: process.env.DB_USER,
password: process.env.DB_PASS
});
// BUG: Connection never closed!
connections.push(connection);
return connection;
}

Proposed Actions (requires approval):

  1. Scale auth-service replicas (2→6) - Safe
  2. Restart pods in rolling fashion - Medium risk
  3. Deploy hotfix for connection leak - High risk
Approve scale + rolling restart. Hold the hotfix deployment - too risky during incident. What's the rollback plan?
Scaling auth-service pods
Kubernetes
>

Rollback Plan Confirmed

Safety Measures:

  • Can revert to 2 replicas if issues arise
  • Pod restart reversible (previous versions available)
  • Circuit breakers will isolate auth-service if needed

Executing Actions:

  • Scaling auth-service: 2→6 replicas
  • Rolling restart: 0/6 pods restarted

Active Monitoring:

  • Error rates
  • Response times
  • Connection pools
Monitoring recovery metrics
Datadog
>

Recovery Status

Actions Completed:

  • Scaling complete: 6/6 replicas healthy
  • Rolling restart: 6/6 pods restarted (0 downtime)

System Metrics:

  • Datadog Connection pool: 47% utilization (normal range)
  • Prometheus API response: 180ms avg (was 15s)
  • Sentry Error rate: 0.8% (was 23%)
Good call. Monitor for 15 min, then we'll do post-incident review. Keep circuit breakers ready.
Updating stakeholders
Slack
>

Incident Resolved

(15 min observation complete)

Total Duration: 13 minutes Impact: ~$3,200 revenue loss Users Affected: ~1,250

Post-Incident Actions Needed:

  • Schedule hotfix deployment (non-critical window)
  • Update monitoring thresholds for connection pools
  • Review auth-service memory allocation
  • Conduct blameless post-mortem

Stakeholder Update: Incident Commander, Customer Support notified of resolution

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Investigation

Payment service showing 5xx errors. Investigate?

Analyzing errors
Datadog
>

Initial findings from error analysis:

Spike started 14:30 UTC. 3 payment pods showing high memory usage. Investigating further...

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Kubernetes

Check Pods

Prometheus

Query Metrics

Datadog

Search Logs

Slack

Create Alert

Kubernetes

Scale Service

Sentry

Check Errors

GitHub

Draft PR

Prometheus

Monitor Health

CloudWatch

View Metrics

Full Agentic Capabilities.

Draft PR, Create Post-mortem, Update stakeholders, and more.

Root Cause Analysis

Analyzed 47 sources in 43s
✓ Processing logs12.3s
✓ Metrics analysis8.7s
✓ Infrastructure scan15.2s
✓ Code repository4.1s
✓ Historical patterns5.9s
Correlating payment latency spikes
Prometheus
>

Payment Latency Spike Detected

P99 latency: 450ms → 2.1s at 09:15:00 (EU only)

payment_latency_p99 / connection_pool_active
2000ms1000ms500ms0ms09:1009:1509:2009:25
Tracing connection pool exhaustion
Sentry
>

Likely Root Cause

Missing defer conn.Close() in notification_handler.go:142

Pool exhausted: 200/200 connections during EU peak

Analyzing connection pool metrics
Datadog
>

Historical Pattern Analysis

Baseline: 45-65 connections, spike: 198/200

Leak pattern: EU error conditions only

Root Cause Analysis.

Resolve Issue faster with autonomous root cause analysis.

Incident Workflow

Investigate Support Tickets

Zendesk
Ticket Received
00:01
Datadog
Check System Health
00:05
Notion
Check Past Incidents
00:03
Kubernetes
Check Pod Status
00:08
Datadog
Get Application Logs
1m ago
GitHub
Check Latest Deployments
1m ago
Slack
Send RCA to #engineering
1m ago

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JD
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1 connected

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PagerDuty

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Redshift

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S3

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Sentry

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SigNoz

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Slack
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