Agent Runtime Log - Production Microservice Deployment (ATTEMPT 1 - FAILURE)
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Session ID: deploy-agent-20241120-1430-attempt1
Task: "Deploy recommendation microservice to production with zero downtime"
Agent: DeploymentAgent v3.2
Start Time: 2024-11-20 14:30:00 UTC
Attempt: 1/3

[14:30:00] [INIT] Agent initialization started
[14:30:00] [INFO] Task: Deploy recommendation-service v2.5.0 to production
[14:30:00] [INFO] Target environment: production-cluster-us-east
[14:30:01] [INFO] Deployment strategy: Blue-green with gradual traffic shift
[14:30:01] [SUCCESS] Agent initialized

[14:30:01] [PLANNING] Creating deployment plan
[14:30:02] [THOUGHT] Deployment steps should include:
  1. Validate service configuration and dependencies
  2. Build and push Docker image
  3. Deploy green environment alongside blue
  4. Run health checks and smoke tests
  5. Gradually shift traffic from blue to green
  6. Monitor metrics and rollback if needed

[14:30:02] [ACTION] Step 1: Validating service configuration
[14:30:02] [TOOL] Executing: validate_config(service="recommendation-service", version="v2.5.0")
[14:30:05] [OBSERVATION] Configuration validation results:
  - Service manifest: Valid
  - Environment variables: 12 variables defined
  - Resource limits: CPU 2 cores, Memory 4Gi
  - Dependencies: PostgreSQL, Redis, Kafka
  - Health check endpoint: /health

[14:30:05] [THOUGHT] Configuration looks good. Proceeding with Docker build.

[14:30:05] [ACTION] Step 2: Building Docker image
[14:30:05] [TOOL] Executing: build_docker_image(
  dockerfile="services/recommendation/Dockerfile",
  tag="recommendation-service:v2.5.0"
)
[14:30:55] [OBSERVATION] Docker build completed:
  - Image: recommendation-service:v2.5.0
  - Size: 856 MB
  - Build time: 50 seconds
  - Base image: python:3.11-slim
  - Layers: 12

[14:30:55] [ACTION] Pushing image to registry
[14:30:55] [TOOL] Executing: push_image(image="recommendation-service:v2.5.0")
[14:31:20] [OBSERVATION] Image pushed successfully to gcr.io/company/recommendation-service:v2.5.0

[14:31:20] [ACTION] Step 3: Deploying green environment
[14:31:20] [TOOL] Executing: deploy_kubernetes(
  service="recommendation-service",
  version="v2.5.0",
  environment="green",
  replicas=3
)
[14:31:25] [OBSERVATION] Kubernetes deployment initiated:
  - Deployment: recommendation-service-green
  - Replicas: 3
  - Namespace: production
  - Pods starting...

[14:31:45] [ACTION] Monitoring pod startup
[14:31:46] [OBSERVATION] Pod status:
  - recommendation-green-0: Running
  - recommendation-green-1: Running
  - recommendation-green-2: Running
  - All pods ready and passing health checks

[14:31:46] [ACTION] Step 4: Running smoke tests on green environment
[14:31:46] [TOOL] Executing: run_smoke_tests(target="recommendation-green")
[14:32:00] [OBSERVATION] Smoke test results:
  - Health check: PASSED (200 OK)
  - Database connectivity: PASSED
  - Redis connectivity: PASSED
  - Kafka connectivity: PASSED
  - Sample recommendation request: PASSED (response time: 120ms)

[14:32:00] [THOUGHT] Green environment healthy. Ready to shift traffic.

[14:32:00] [ACTION] Step 5: Starting gradual traffic shift (Blue → Green)
[14:32:00] [TOOL] Executing: traffic_shift(
  from_env="blue",
  to_env="green",
  strategy="gradual",
  initial_percentage=10
)
[14:32:05] [OBSERVATION] Traffic shift initiated:
  - Blue environment: 90% traffic
  - Green environment: 10% traffic
  - Monitoring for 5 minutes before next increment

[14:32:05] [ACTION] Monitoring metrics during traffic shift
[14:33:00] [OBSERVATION] Metrics at 10% traffic (1 minute):
  - Request rate: 150 req/s to green (1500 req/s total)
  - Average latency: 125ms (baseline: 110ms)
  - Error rate: 0.1%
  - All metrics within acceptable range

[14:35:00] [ACTION] Increasing traffic to 25%
[14:35:01] [TOOL] Executing: traffic_shift(percentage=25)
[14:35:05] [OBSERVATION] Traffic updated:
  - Blue: 75%, Green: 25%

[14:36:00] [OBSERVATION] Metrics at 25% traffic (1 minute):
  - Request rate: 375 req/s to green
  - Average latency: 130ms
  - Error rate: 0.2%
  - Metrics stable

[14:38:00] [ACTION] Increasing traffic to 50%
[14:38:01] [TOOL] Executing: traffic_shift(percentage=50)
[14:38:05] [OBSERVATION] Traffic updated:
  - Blue: 50%, Green: 50%

[14:39:00] [OBSERVATION] Metrics at 50% traffic (1 minute):
  - Request rate: 750 req/s to green
  - Average latency: 135ms
  - Error rate: 0.3%

[14:40:30] [ERROR] Alert triggered: Error rate spike detected!
[14:40:31] [TOOL] Executing: check_error_logs()
[14:40:32] [OBSERVATION] ERROR ANALYSIS:
  - Error rate jumped from 0.3% to 4.2% (14x increase)
  - Error type: "DatabaseConnectionError: too many connections"
  - Affected pods: All 3 green pods
  - Pattern: Started at 50% traffic mark

[14:40:32] [THOUGHT] CRITICAL: Connection pool exhausted at 50% traffic!

[14:40:32] [ACTION] Investigating database connection configuration
[14:40:32] [TOOL] Executing: check_db_config(service="recommendation-green")
[14:40:35] [OBSERVATION] Database configuration:
  - Max connections per pod: 20
  - Total pods: 3
  - Total connection capacity: 60 connections
  - Current active connections: 58 (approaching limit)
  - Database max_connections: 100
  - Blue environment using: 40 connections
  - Green environment using: 58 connections
  - TOTAL: 98/100 connections (98% utilization!)

[14:40:35] [THOUGHT] Root cause identified: Insufficient connection pool size!
  - At 50% traffic, green needs ~60 connections
  - At 100% traffic, would need ~120 connections
  - Database max_connections=100 is insufficient
  - We're already at 98% capacity with both environments running

[14:40:35] [ACTION] Initiating emergency rollback
[14:40:35] [TOOL] Executing: rollback_traffic(to_env="blue")
[14:40:40] [OBSERVATION] Traffic rollback initiated:
  - Shifting all traffic back to blue environment
  - Green: 50% → 25% → 10% → 0%

[14:41:00] [OBSERVATION] Rollback progress:
  - Traffic: 100% on blue, 0% on green
  - Error rate: Dropping (4.2% → 2.1% → 0.4%)
  - Service stabilizing

[14:41:30] [OBSERVATION] Rollback complete:
  - Blue environment: 100% traffic, stable
  - Green environment: 0% traffic, shutting down
  - Error rate: Back to 0.2% (normal baseline)
  - Service restored

[14:41:30] [FAILURE] Deployment failed - Root cause analysis:

PRIMARY ISSUE: Database connection pool misconfiguration
  - Database max_connections: 100 (too low for blue+green running simultaneously)
  - Each environment needs ~60 connections at 50% traffic
  - During blue-green deployment, total needed: 100+ connections
  - Database cannot support both environments concurrently

CONTRIBUTING FACTORS:
  1. Did not account for connection pool sizing during dual environment operation
  2. No load testing performed with both blue and green running simultaneously
  3. Connection pool monitoring alerts not in place
  4. Gradual rollout exposed issue only at 50% mark

LESSONS LEARNED:
  1. ⚠️ CRITICAL: Database connection pools must accommodate BOTH environments during deployment
  2. ⚠️ Connection pool size = (peak connections per pod) × (total pods in blue + green)
  3. ⚠️ Must load test with full capacity of both environments before production
  4. ⚠️ Set up connection pool monitoring and alerts BEFORE deployment
  5. ⚠️ Have emergency rollback procedure ready (which saved us here)

REMEDIATION PLAN:
  1. Increase database max_connections from 100 to 250
  2. Adjust connection pool per pod from 20 to 30
  3. Add connection pool utilization monitoring
  4. Set alert threshold at 70% connection utilization
  5. Document connection pool requirements in deployment checklist

DEPLOYMENT STATUS: FAILED
ROLLBACK STATUS: SUCCESSFUL (no customer impact beyond brief error spike)
TIME TO DETECT: 8 minutes
TIME TO ROLLBACK: 1.5 minutes
CUSTOMER IMPACT: 1.5 minutes of elevated errors (4.2% error rate)

[14:41:30] [EXPORT] Saving failure report: reports/deployment-attempt1-failure.json
[14:41:31] [NOTIFICATION] Alerting team about deployment failure and lessons learned
[14:41:31] [SHUTDOWN] Agent session terminated

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KEY TAKEAWAYS FOR NEXT ATTEMPT:
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1. Fix database connection pool sizing BEFORE retry
2. Calculate total connections needed: (pods_blue + pods_green) × connections_per_pod
3. Add connection monitoring to detect issues early
4. Test with both environments running at full capacity
5. Verify database can handle combined load before production deployment
