AI-Assisted Incident Response and Runbook Generation with Kiro

At 3:17 AM, your pager fires. P99 latency on the order service spiked to 8 seconds. You open your laptop, half-awake, and start the familiar scramble: check dashboards, read logs, correlate with deployments, form hypotheses. Twenty minutes later, you find the issue — a missing database index after last night's migration. The fix takes two minutes. The investigation took ten times longer.

Kiro changed our incident response by doing two things: generating runbooks that anticipate failure modes, and assisting during live incidents by parallelizing the investigation steps a human would do sequentially.

The Problem: Incidents Are Information Retrieval Problems

Most production incidents are not novel. They follow patterns: a deployment introduced a regression, a dependency degraded, traffic exceeded capacity, or a configuration change had unintended effects. The challenge is not usually fixing the problem — it is finding the problem.

Our team's incident data showed:

• Average time to detection (TTD): 4 minutes (automated monitoring)
• Average time to root cause (TTRC): 34 minutes
• Average time to resolution (TTR): 8 minutes after root cause identified
• Total average incident duration: 46 minutes

The investigation phase — correlating logs, checking metrics, reviewing recent changes — consumed 74% of incident duration. That phase is primarily information retrieval and correlation, exactly what AI agents excel at.

Kiro-Generated Runbooks

Traditional runbooks are static documents that go stale. Kiro generates runbooks from your actual infrastructure, monitoring configuration, and service topology. When infrastructure changes, the runbooks update.

Here is a runbook Kiro generated for our payment service:

## Incident Runbook: Payment Service Degradation

### Detection Signals
- CloudWatch alarm: payment-service-error-rate > 1%
- CloudWatch alarm: payment-service-p99-latency > 2000ms
- Downstream alert: order-service timeout on payment calls

### Immediate Actions (First 2 minutes)
1. Check service health: `curl https://payment.internal/health`
2. Verify instance count: `aws ecs describe-services --services payment-service`
3. Check recent deployments:
   ```bash
   aws ecs describe-task-definition --task-definition payment-service \
     --query 'taskDefinition.revision'

Investigation Tree

Is the service healthy?
├── NO → Check ECS task status, restart if crashed
│   └── Still failing? → Check container logs for startup errors
└── YES → Service is running but degraded
    ├── High latency?
    │   ├── Database slow? → Check RDS metrics (connections, CPU, IOPS)
    │   ├── External API slow? → Check Stripe/gateway response times
    │   └── CPU bound? → Check ECS CPU metrics, scale if > 80%
    └── High error rate?
        ├── 4xx errors? → Check request validation, client changes
        ├── 5xx errors? → Check application logs for exceptions
        └── Timeout errors? → Check downstream service health

Common Root Causes (ranked by frequency)

1. Database connection exhaustion (28% of incidents)
   • Symptom: "too many connections" in logs
   • Fix: Restart service to release connections, investigate leak
2. Stripe API degradation (22%)
   • Symptom: Elevated p99, Stripe status page shows issues
   • Fix: Enable circuit breaker, retry with backoff
3. Post-migration missing index (18%)
   • Symptom: Specific query latency spikes after deployment
   • Fix: Identify slow query, add index concurrently
4. Memory leak after deployment (15%)
   • Symptom: Gradual latency increase, OOM kills after 2-4 hours
   • Fix: Rollback to previous task definition

This runbook is not generic. It references our specific infrastructure, our common failure modes (derived from incident history), and our exact commands. When we add a new dependency or change our deployment mechanism, Kiro updates the relevant runbook sections.

## Live Incident Assistance

During an active incident, Kiro parallelizes the investigation:

```typescript
// Incident investigation orchestration
{
  task: "Investigate payment service latency spike",
  stages: [
    { name: "check-metrics", role: "context-gatherer",
      prompt: "Query CloudWatch for payment-service metrics in the last 30 minutes: CPU, memory, request count, error rate, p50/p95/p99 latency. Identify the inflection point." },
    { name: "check-logs", role: "context-gatherer",
      prompt: "Search CloudWatch Logs for payment-service errors and warnings in the last 30 minutes. Identify recurring patterns or new error types." },
    { name: "check-deploys", role: "context-gatherer",
      prompt: "List all deployments to the payment-service ECS service in the last 24 hours. Include task definition versions and deployment timestamps." },
    { name: "check-dependencies", role: "context-gatherer",
      prompt: "Check health and latency metrics for payment service dependencies: RDS, Redis, Stripe API. Identify any degradation." },
    { name: "correlate", role: "implementer",
      depends_on: ["check-metrics", "check-logs", "check-deploys", "check-dependencies"],
      prompt: "Correlate findings across all investigation tracks. Identify the most likely root cause and recommend a fix." }
  ]
}

Four investigation tracks run simultaneously, completing in three minutes what would take a human fifteen minutes sequentially. The correlation stage synthesizes findings into a diagnosis:

## Incident Correlation Report

**Timeline:**
- 03:12 — Deployment: task-definition payment-service:247
- 03:15 — P99 latency begins climbing (500ms → 2000ms → 8000ms)
- 03:17 — Error rate crosses 1%, alarm fires

**Root Cause (High Confidence):**
Deployment :247 included migration 042_add_payment_metadata.sql which
added a column to the transactions table. The application now queries
with a WHERE clause on the new column, but no index exists.

**Evidence:**
- Logs show slow query warnings: `SELECT * FROM transactions WHERE metadata_type = $1` (avg 4200ms)
- RDS CPU spiked from 15% to 78% at 03:15
- No degradation in Stripe, Redis, or other dependencies
- Issue coincides exactly with deployment timestamp

**Recommended Fix:**
1. Create index: `CREATE INDEX CONCURRENTLY idx_transactions_metadata_type ON transactions(metadata_type);`
2. Expected resolution time: ~3 minutes for index creation on current table size
3. No rollback needed — index addition resolves the issue

Before and After: Incident Response Metrics

Metric	Before Kiro	After Kiro	Change
Mean time to root cause (TTRC)	34 min	8 min	-76%
Mean time to resolution (TTR)	46 min	14 min	-70%
Incidents requiring escalation	42%	15%	-64%
Repeat incidents (same root cause)	23%	6%	-74%

The repeat incident reduction deserves explanation. When Kiro assists with an incident, it generates a post-incident analysis that includes preventive measures. These measures are encoded back into the system — as monitoring rules, deployment checks, or migration validation hooks — so the same failure mode cannot recur.

Post-Incident Learning Loop

After every incident, Kiro generates three artifacts:

1. Incident Report — What happened, when, impact, root cause, resolution steps

2. Prevention Recommendation — Specific actions to prevent recurrence:

## Prevention: Missing Index After Migration

### Immediate
- Add PostFileSave hook for migration files that checks for new WHERE clauses
  without corresponding indexes

### Systematic
- Add to migration checklist: all new columns referenced in WHERE/JOIN clauses
  must include an index creation step
- Add synthetic query performance test to deployment pipeline

3. Runbook Update — The incident's root cause and fix are added to the relevant runbook, updating the "Common Root Causes" section with real data.

This creates a positive feedback loop: every incident makes the runbooks more accurate and the prevention systems more comprehensive.

Implementing Incident Response Automation

Our setup requires three components:

1. Runbook generation — A weekly Kiro session that analyzes infrastructure configuration and generates/updates runbooks for each service.

2. Incident trigger — When PagerDuty fires, a webhook triggers Kiro's investigation orchestration. The parallel investigation starts before the on-call engineer fully wakes up.

3. Post-incident automation — After resolution, Kiro generates the incident report and prevention recommendations from the investigation context.

The on-call engineer's role shifts from "gather information" to "validate the AI's diagnosis and execute the fix." This is faster, less error-prone, and less exhausting at 3 AM.

What Kiro Cannot Handle in Incidents

Kiro accelerates investigation but does not make decisions during incidents. It cannot:

• Decide whether to rollback or push forward (business impact judgment)
• Communicate with stakeholders about incident status
• Make trade-off decisions (accept degraded performance vs. full outage during fix)
• Invoke runbook steps that modify production state without human approval

The human incident commander retains authority. Kiro provides the intelligence layer that makes their decisions faster and better-informed.

Conclusion

Incident response is fundamentally an information retrieval and correlation problem. Kiro solves it by parallelizing investigation, maintaining current runbooks, and creating a learning loop that prevents repeat incidents. Our on-call burden dropped measurably — not because incidents disappeared, but because resolving them became a ten-minute exercise instead of a forty-five-minute scramble.

If your team dreads on-call rotations, start by encoding your most common incident types into structured runbooks. Then add the parallel investigation pipeline. The first 3 AM incident that resolves in eight minutes instead of forty will convert every skeptic on the team.