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AWS Auto Heal Workflow: Automated Incident Resolution for Enterprise Cloud Operations
ABOUT THE CUSTOMER

A global digital enterprise, operating in a highly regulated sector, relied on AWS-based, cloud-native platforms to serve millions of customers. With mission-critical applications and strict regulatory demands, the business required maximum uptime, operational agility, and robust compliance.

THE CHALLENGE |
  • Manual Operations: Incident detection, analysis, and remediation were labor-intensive, increasing Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR).
  • Fragmented Visibility: Multiple, siloed monitoring tools caused slow anomaly detection and an incomplete infrastructure view.
  • Security and Compliance Risks: Inconsistent permissions management and insufficient audit trails jeopardized data integrity.
  • Inconsistent Incident Response: Knowledge wasn’t systematically captured or reused, causing operational inefficiencies and knowledge gaps.
  • Slow Escalations: Escalation processes were reactive, with context often missing, leading to reduced business continuity.
THE SOLUTION |
  • Pilot Launch: Initial rollout targeted critical application clusters, demonstrating immediate value.
  • Change Management: SRE and DevOps teams participated in targeted training and simulations to ensure operational readiness.
  • Phased Scaling: Agile sprints managed incremental adoption, adapting automation coverage based on feedback and performance.

Before: Disconnected monitoring, heavy manual intervention, and slow incident escalations.

After: Integrated observability, AI-powered automation, and seamless human oversight for critical scenarios.

Agile Delivery Model: Focused sprints enabled iterative agent integration, process alignment, and quick wins.

Measurable Outcomes:

  • 70% reduction in MTTD (Mean Time to Detect)
  • 60% decrease in MTTR (Mean Time to Resolve)
  • 50%+ automation of routine incident responses
Business Impact:
  • Reliability Gains: Proactive healing and data-driven automation deliver superior uptime and customer experience.
  • Risk Reduction: Enhanced security, policy enforcement, and comprehensive audit logging support regulatory needs.
  • Operational Cost Savings: Automated workflows significantly reduce the need for manual intervention, freeing resources for innovation.
Implementation Highlights
  • Sprint-Driven Integration: Agile sprints facilitated focused technology adoption and short, regular feedback loops.
  • Robust Enablement: Structured enablement—documentation, hands-on training, and knowledge transfer—drove broad adoption.
  • Stakeholder Engagement: Early wins through pilots accelerated buy-in for organization-wide rollout.
Tools Used (Technology Stack)
Layer Technology Stack
LLM Platform Amazon Bedrock
Embedding Models Bedrock Embedding Models
Orchestration n8n
Monitoring AWS CloudWatch, Prometheus, Grafana
Vector Database Qdrant
External Search Tavily API, Serp API
Security & Compliance AWS IAM, TLS 1.2/1.3
Supporting tech also includes: 

 CI/CD pipelines for automated, dependable deployments.

Real-time audit logging for complete traceability.

Custom API integrations expanding automation to custom and legacy components.

BENEFITS DELIVERED |
  • End-to-End Automation: Six specialized agents orchestrate monitoring, analytics, resolution, validation, and escalation—with AI-driven workflows minimizing manual intervention.
  • Unified Observability Layer: Integration of AWS CloudWatch, Prometheus, and Grafana provided a single-pane view and proactive anomaly alerts.
  • LLM-Driven Diagnostics: Utilizing Amazon Bedrock and advanced embedding models, the workflow automated root cause analysis, with confidence scores enabling human escalation for critical incidents.
  • Self-Healing Mechanism: Low-risk incidents are resolved autonomously; medium/high risk issues go through approval workflows with built-in rollbacks, balancing agility and safety.
  • Centralized Learning: Every incident is documented in a knowledge base, enabling pattern recognition and continuous process improvement.
  • Compliance-by-Design: All actions are risk-categorized, audit-logged, and tightly controlled through least-privileged IAM roles, exceeding compliance
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