Cloud Native Architecture Engineering Playbook & Benchmark Reports — Battle-tested engineering know-how from production environments

Engineering Playbook is a comprehensive collection of cloud native architecture engineering practices accumulated from production environments. It covers Amazon EKS infrastructure optimization, Agentic AI platform engineering, AIDLC/AgenticOps methodology, hybrid infrastructure, security governance, and quantitative benchmark results.

Each technical domain provides implementation guides alongside measurable performance data to support data-driven architecture decisions.

Live Documentation: https://devfloor9.github.io/engineering-playbook/

End-to-end guide for building enterprise Agentic AI platforms on EKS.

• Design & Architecture: Technical challenges, EKS-based solutions, platform architecture, AWS-native patterns
• Model Serving & Inference: EKS GPU node strategy (Auto Mode / Karpenter / MNG / DRA), GPU resource management, vLLM, llm-d distributed inference, MoE model serving, NVIDIA GPU stack (Dynamo / GPU Operator / DCGM / KAI Scheduler), NeMo Framework
• Inference Gateway & Routing: LLM Gateway 2-tier architecture (kgateway + agentgateway + Bifrost), inference gateway routing, OpenClaw AI Gateway
• Agent & Data: Kagent Kubernetes agents, Milvus vector database
• Operations & MLOps: Agent monitoring, RAGAS evaluation, LLMOps observability, MLOps pipeline, SageMaker-EKS integration
• Enterprise Ops: Agentic playbook, compliance framework, domain customization
• Inference Optimization: EKS architecture for inference & model performance optimization — vLLM, KV Cache-aware routing, disaggregated serving, LWS multi-node, Bifrost→Bedrock fallback, Hybrid Node integration

Production-grade guides for Amazon EKS infrastructure optimization.

• Networking & Performance: Cilium ENI, Gateway API migration, CoreDNS tuning, East-West traffic optimization
• Control Plane & Scaling: Large-scale cluster scaling strategies, cross-cluster object replication
• Resource & Cost: Karpenter autoscaling, resource optimization, cost management
• Operations & Reliability: GitOps (Argo CD), node monitoring, EKS debugging & resiliency, Pod health lifecycle
• Security & Authentication: EKS API server authentication/authorization, Pod Identity, IRSA

AI Development Lifecycle framework and Agentic AI operational feedback loops.

• AIDLC Framework: Reliability dual-axis (Ontology × Harness) based AI development lifecycle
• AgenticOps: OpenTelemetry observability, CloudWatch AI integration, DevOps Guru predictive operations

On-premises GPU infrastructure and cloud native platform integration.

• EKS Hybrid Nodes adoption guide
• SR-IOV with DGX H200
• File storage strategies
• Harbor registry integration

Enterprise security governance practices.

• Identity-First Security (EKS Pod Identity)
• GuardDuty Extended Threat Detection
• Kyverno policy management
• Supply Chain Security
• Default namespace incident analysis

Red Hat OpenShift on AWS installation, security, and compliance guide.

Quantitative benchmarks for infrastructure, AI/ML, and hybrid environments.

• Networking: CNI performance comparison (VPC CNI vs Cilium), Gateway API implementation benchmarks
• AI/ML Inference: AI/ML workload analysis, AgentCore vs EKS self-hosted inference, Dynamo inference benchmark
• Infrastructure & Operations: Infrastructure performance, hybrid infrastructure, security operations metrics

docs/
├── agentic-ai-platform/           # Agentic AI Platform
│   ├── design-architecture/        #   Design & Architecture
│   ├── model-serving/              #   Model Serving (GPU strategy, vLLM, llm-d, MoE, NeMo)
│   ├── gateway-agents/             #   Gateway & Agents (LLM Gateway, Inference GW, OpenClaw)
│   ├── agent-data/                 #   Agent & Data (Kagent, Milvus)
│   ├── operations-mlops/           #   Operations & MLOps (monitoring, RAGAS, LLMOps)
│   └── enterprise-ops/             #   Enterprise Ops (playbook, compliance, customization)
├── eks-best-practices/             # EKS Best Practices
│   ├── networking-performance/     #   Networking (Cilium, Gateway API, CoreDNS)
│   ├── control-plane-scaling/      #   Control Plane Scaling
│   ├── resource-cost/              #   Resource & Cost Optimization
│   ├── operations-reliability/     #   Operations & Reliability
│   └── security-authn/             #   Security & Authentication
├── aidlc/                          # AIDLC Framework
│   └── agentic-ops/                #   AgenticOps
├── hybrid-infrastructure/          # Hybrid Infrastructure
├── security-governance/            # Security & Governance
├── benchmarks/                     # Benchmark Reports
└── rosa/                           # ROSA (OpenShift on AWS)

Presentation materials are available at /slides:

• Agentic AI Platform — Full platform overview (86 slides)
• Inference & Model Performance Optimization — EKS architecture for LLM inference optimization

# Install dependencies
npm install

# Start dev server
npm start

# Production build
npm run build

Requires Node.js >=20.0 and Docusaurus 3.9.2

Issues, PRs, and feedback are all welcome. See GitHub Issues for details.

Content in this project is available under the MIT License.