Patterns
Kubernetes Landing Zone
Healthcare Lakehouse Guide / Kubernetes Landing Zone

Kubernetes Platform Landing Zone & Fleet Infrastructure

To implement the 5-Tier Medallion architecture, a robust Kubernetes platform is required. The platform must support GPU-accelerated workloads, strict HIPAA compliance, and GitOps fleet management.

Management plane
ns: rancher-system Β· argocd Β· crossplane-system Β· backstage Β· vault-system
Rancher Prime (MCM)Cluster API (CAPI)ArgoCD + ApplicationSetsFlux CDCrossplaneBackstage IDPHashiCorp VaultExternal Secrets OperatorSOPScert-manager (root CA)
Cluster provisioning β€” 6 flavors
All clusters bootstrapped and day-2 configured from management plane via GitOps ApplicationSets
Cloud managed
EKS (AWS)AKS (Azure)GKE (Google Cloud)
On-premise / self-managed
kubeadmRKE2 (Rancher)OpenShift
Per-cluster platform add-ons
ns: platform-system Β· monitoring Β· logging Β· security Β· velero
Networking
Cilium CNI (eBPF)Istio service meshGateway APIMetalLB (on-prem)NGINX Ingress
Storage
EBS / Azure Disk / PD CSILonghorn (on-prem HA)Rook-CephVelero backups
Policy & security
OPA GatekeeperKyvernoFalco (runtime)Trivy Operatorcert-managerCosign + SigstoreHarbor Registry
Observability
Prometheus OperatorGrafanaLoki + PromtailJaeger / TempoOpenTelemetry CollectorDCGM ExporterAudit β†’ SIEM
Nvidia GPU & AI infrastructure
ns: gpu-operator Β· nvidia-network-operator Β· ai-platform
Nvidia operators
GPU OperatorNetwork OperatorDCGM + DCGM ExporterNode Feature DiscoveryMIG ManagerGPU time-slicing
CUDA stack
CUDA 12.xcuDNN 9NCCL (multi-GPU)TensorRT 10Nvidia Container Toolkit
Inference & serving
Nvidia NIM microservicesBioNeMo NIM (clinical)Triton Inference ServervLLMKServe InferenceService
Nvidia NemoClaw β€” agentic AI framework
ns: nemo-agents Β· milvus Β· redis
NeMo framework
NeMo Core (Megatron-LM)NeMo Customizer (LoRA)NeMo RAFTNeMo EvaluatorNeMo GuardRails
NemoClaw orchestration
Agent SupervisorReAct reasoning engineTool registryMilvus vector DB (RAG)Redis (conversation memory)Multi-agent workflow engineClinical safety guardrails
Healthcare clinical AI agents & data
ns: clinical-apps (PHI) Β· clinical-data (PHI, deny-all network policy default)
Clinical AI agents (NemoClaw-orchestrated)
Diagnostic decision supportAmbient clinical documentationDrug interaction checkerRadiology AI (DICOM)Prior authorization automationPatient risk stratificationClinical coding (ICD-10 / CPT)Care plan generator
Healthcare data integration
FHIR R4 (Hapi FHIR)HL7 v2/v3 brokerDICOM server (Orthanc)SMART on FHIR (Epic / Cerner)De-identification pipelineClinical data lake
Namespace strategy
NamespaceContentsPHI
kube-systemAPI server, CoreDNS, kube-proxy, cloud-controller-managerβ€”
platform-systemIstio, cert-manager, ingress, MetalLB, external-dnsβ€”
monitoringPrometheus, Grafana, Loki, Jaeger, OTel Collectorβ€”
securityFalco, OPA Gatekeeper, Kyverno, Vault Agent injectorβ€”
gpu-operatorDriver daemonset, CUDA, device plugin, DCGM, MIG managerβ€”
ai-platformNIM, Triton, vLLM, KServe, BioNeMo containersβ€”
nemo-agentsNemoClaw supervisor, tool registry, Milvus, Redisβ€”
clinical-appsClinical AI agent workloads (restricted PSS)βœ“
clinical-dataFHIR, HL7, DICOM, de-ID pipeline (deny-all NetworkPolicy)βœ“βœ“
Cloud managed Kubernetes
Cloud β€” AWS
Amazon EKS
NetworkingAWS VPC CNI Β· Cilium (overlay option)
StorageEBS CSI (gp3) Β· EFS CSI (RWX) Β· S3 Mountpoint
GPU nodesp5.48xlarge (H100) Β· p4d (A100) Β· g5 (A10G)
IAMIRSA β€” IAM roles for service accounts
AutoscalerKarpenter NodePool CRDs
Load balancerAWS Load Balancer Controller (ALB/NLB)
GitOpsEKS Blueprints (Terraform) + ArgoCD
NotableEKS Anywhere bridges to on-prem
Cloud β€” Azure
Azure AKS
NetworkingAzure CNI Overlay Β· Cilium BYOC
StorageAzure Disk v2 (Premium SSD) Β· Azure NetApp Files
GPU nodesNC A100 v4 Β· ND H100 v5 Β· NCas T4 v3
IAMWorkload Identity (UAMI + federated creds)
AutoscalerCluster Autoscaler + KEDA
Load balancerAzure Load Balancer + AGIC
GitOpsFlux (AKS GitOps extension) or ArgoCD
NotableAKS Hybrid Β· confidential compute nodes
Cloud β€” GCP
Google GKE
NetworkingGKE Dataplane V2 (Cilium eBPF) Β· VPC-native
StoragePersistent Disk CSI Β· Hyperdisk ML Β· Filestore
GPU nodesH100 (A3 mega VMs) Β· A100 (A2 VMs) Β· L4 (G2 VMs)
IAMWorkload Identity Federation
AutoscalerCluster Autoscaler + NAP (node auto-provisioning)
Load balancerGKE native Gateway API (L7)
GitOpsConfig Sync + Config Connector
NotableAutopilot mode Β· GKE Enterprise fleet
On-premise / self-managed Kubernetes
On-premise β€” bare metal / VM
kubeadm
NetworkingCilium CNI (eBPF, recommended) / Calico
StorageLonghorn Β· Rook-Ceph Β· NFS subdir provisioner
GPU nodesGPU Operator manages drivers + device plugin
IAMVault + External Secrets + Keycloak OIDC (Dex)
Load balancerMetalLB (L2 or BGP mode)
HA controlKeepalived + HAProxy for API server VIP
AutoscalerCluster Autoscaler (Cluster API provider)
NotableMaximum flexibility Β· highest operational burden
On-premise β€” Rancher
RKE2
NetworkingCanal (default) Β· Cilium Β· Calico selectable
StorageLonghorn (Rancher-native, 3-replica HA)
GPU nodesGPU Operator + RKE2 node taints / labels
IAMRancher RBAC Β· Keycloak / AD + Vault
Load balancerMetalLB + Nginx Ingress (bundled)
HA controletcd HA built-in Β· Rancher Prime MCM
AutoscalerCluster Autoscaler via Rancher machine pools
NotableFIPS 140-2 mode Β· CIS hardening built-in
On-premise β€” Red Hat
OpenShift
NetworkingOVN-Kubernetes (default) Β· Multus CNI
StorageOpenShift Data Foundation (Ceph / NooBaa)
GPU nodesGPU Operator via OperatorHub + NFD
IAMOAuth + SCC Β· Keycloak / LDAP / Active Directory
Load balancerHAProxy router (built-in) + MetalLB
HA control3-node etcd HA Β· multi-AZ capable
AutoscalerMachineSet + MachineAutoscaler CRDs
NotableSCC stricter than K8s PSS Β· OperatorHub Β· FIPS
Common add-ons bootstrapped to all 6 flavors via ArgoCD ApplicationSet β€” cert-manager Β· Falco Β· OPA Gatekeeper Β· Kyverno Β· Prometheus Operator Β· Grafana Β· Loki Β· Jaeger Β· OTel Collector Β· Velero Β· Harbor Β· Trivy Operator Β· Cosign Β· Nvidia GPU Operator Β· External Secrets Operator
Hardware
GPU compute nodes
H100 SXM5 80GBA100 SXM4 80GBNVLink 4th genInfiniBand HDR 200Gb/sGPUDirect RDMAnvidia.com/gpu taint on node
Nvidia operators
GPU Operator + Network Operator
GPU Operator (v24.x)Driver daemonsetNvidia Device PluginDCGM + DCGM ExporterNode Feature DiscoveryMIG Manager (A100/H100)GPU time-slicingNetwork Operator (RDMA SR-IOV)
CUDA stack
CUDA runtime and compute libraries
CUDA 12.xcuDNN 9NCCL 2.x (multi-GPU comms)TensorRT 10cuBLASNvidia Container Toolkit
Inference infra
Model serving infrastructure
Nvidia NIM (API-compatible containers)BioNeMo NIM (clinical models)Triton Inference ServervLLM (high-throughput LLM)KServe InferenceService CRDServingRuntime CRDKnative (scale-to-zero)
NeMo framework
Foundation model development and fine-tuning
NeMo Core (Megatron-LM)NeMo Customizer (LoRA / P-Tuning)NeMo RAFT (retrieval fine-tuning)NeMo EvaluatorNeMo GuardRailsPyTorch FSDP / DDP
NemoClaw agents
Agentic AI orchestration layer
NemoClaw Agent SupervisorReAct reasoning engineTool registryMilvus vector DB (semantic memory)Redis (conversation memory)RAG pipelineMulti-agent workflow engineClinical safety guardrails
Clinical tools
Tool registry β€” registered tools available to NemoClaw agents
FHIR R4 API (patient data)DICOM WADO-RS (imaging)HL7 v2 message brokerDrugBank / OpenFDA APIClinical KB (UpToDate)ICD-10 / CPT encoderSMART on FHIR (Epic / Cerner)Lab reference ranges
Clinical agents
Healthcare AI agents deployed on NemoClaw
Diagnostic decision supportAmbient clinical documentationDrug interaction checkerRadiology report generationPrior authorization automationPatient risk stratificationClinical coding (ICD/CPT)Care plan generator
GPU node sizing guide β€” 1Γ— H100 80GB for NIM LLM serving (Llama-3.1-70B) Β· 2–4Γ— A100 for NeMo fine-tuning jobs Β· 1Γ— A10G for auxiliary agent tasks Β· MIG mode splits H100 into 7 Γ— 10GB instances for smaller clinical models Β· DCGM metrics scraped by Prometheus every 30s
Access control & identity
Kubernetes RBAC β€” least-privilege ClusterRoles + Roles per namespace; no wildcard verbs
OPA Gatekeeper β€” ConstraintTemplates deny privileged pods, enforce labels, require resource limits in clinical namespaces
Kyverno β€” restrict image registries to Harbor; block :latest tags in production
Pod Security Standards Restricted profile enforced on clinical-data namespace
HashiCorp Vault dynamic secrets β€” database credentials TTL 1h; no long-lived secrets in pods
OpenShift SCC β€” enforces UID ranges, no root, no host path mount for PHI workloads
OIDC SSO + MFA via Keycloak β€” governs kubectl, Rancher, ArgoCD, and Grafana access
Encryption at rest and in transit
etcd encryption at rest β€” AES-256-GCM via EncryptionConfiguration; envelope key in cloud KMS / Vault Transit
PVC encryption β€” KMS-backed StorageClass (gp3/Premium SSD); Longhorn volume encryption on-prem via LUKS
mTLS 100% coverage β€” Istio PeerAuthentication STRICT in clinical-data and clinical-apps namespaces
Service-to-service JWT validation β€” Istio AuthorizationPolicy checks SPIFFE identity (SVIDs) on every call
Container image signing β€” Cosign + Sigstore; Kyverno admission webhook blocks any unsigned image
Secrets in Git encrypted with SOPS + age; External Secrets Operator syncs decrypted values from Vault
Network isolation
Default-deny NetworkPolicy in clinical-data β€” only explicitly whitelisted ingress/egress permitted
Cilium ClusterwidePolicies β€” L7 HTTP/gRPC enforcement with Hubble flow visibility per namespace
Istio AuthorizationPolicy β€” namespace allow-lists; denies traffic from unapproved service accounts
Dedicated GPU node pools with taints β€” only ai-platform and nemo-agents pods can tolerate and schedule
Private cluster API server β€” not publicly accessible; access via bastion + VPN + PAM only
Istio egress gateway β€” all outbound from PHI namespaces is forced through a gateway with audit logging
Audit trail & monitoring
Kubernetes audit log β€” RequestResponse level for clinical-data; shipped to SIEM (Splunk / Elastic)
Falco runtime rules β€” alerts on container escape, privilege escalation, PHI file access, anomalous network
Istio access logs β€” every service call logged with source identity, destination, latency, status
OPA audit mode β€” all policy violations (deny or warn) logged with resource and user context
HIPAA audit trail β€” PHI access events tagged with user ID, timestamp, action, resource β†’ immutable log store
DCGM GPU metrics β†’ Prometheus; critical CVE alerts from Trivy Operator page on-call within 15 min
Backup & disaster recovery
Velero backups β€” cluster state + PV snapshots to encrypted S3/Blob/GCS; schedule every 6h, retain 30 days
etcd snapshots β€” automated via Rancher / OpenShift; stored in a separate encrypted bucket from application data
FHIR data replication β€” Hapi FHIR async to secondary region; RPO target < 1h
RTO target: < 4h clinical systems; < 1h agent inference (stateless pods recreated from image)
DICOM archive β€” dual-site replication with CRC validation; Orthanc on replicated PostgreSQL cluster
Organizational & regulatory
BAA signed with AWS / Azure / GCP for PHI in cloud; BAA addendum with Nvidia for NIM clinical use
Dedicated tenancy β€” PHI node pools on dedicated bare metal or dedicated hosts (no shared hypervisor)
Just-in-time kubectl access via Vault + OIDC; all privileged sessions recorded (PAM)
SBOM β€” Syft generates software bill of materials for every production image; stored in Harbor OCI registry
HITRUST CSF mapping β€” platform capabilities mapped to 80+ controls; evidence collected quarterly
Automated incident runbooks triggered by Falco alerts β†’ PagerDuty escalation + Slack notification

Interactive Tools

Practical tools to help you think systematically, build better AI agents, and master prompt engineering.

Software Engineering Playbooks

Practical, End-to-End implementation guides for building Production-ready Software. Each playbook includes working code, architecture diagrams, and step-by-step instructions.

πŸ€–

Research Agent with Gateway

Build a production-ready AI research agent using Agent Gateway for unified traffic management, authentication, and observability across LLM providers.

Agent Gateway A2A Protocol Multi-Agent
πŸ”—

RAG Pipeline with Vector Database

Implement a complete Retrieval-Augmented Generation pipeline with vector embeddings, semantic search, and context injection for accurate AI responses.

RAG Vector DB Embeddings
πŸ”„

Multi-Agent Orchestration

Create a coordinated multi-agent system with specialized agents, task distribution, and result synthesis for complex problem-solving.

Orchestration Task Distribution Synthesis
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Future References

Explore these resources for deeper learning on AI agent development, spec-driven development, and prompt engineering tools.

Spec-Driven Development

Comprehensive guide on Spec-Driven Development practices and methodologies.

Awesome Copilot

Curated list of GitHub Copilot resources, extensions, and best practices.

Promptfoo

Tool for testing, evaluating, and improving LLM prompts and applications.

Prompts.chat

Collection of prompt engineering resources and templates.

Agent Skills

Agent skills resources and documentation for building AI agent skills.

Awesome Skills

Curated list of awesome skill repositories and collections.

Agent Skills Topic

GitHub topic for discovering agent-related skills and repositories.

AI Agent Topic

Trendshift topic for discovering AI agents.

AI Skills Topic

Trendshift topic for discovering AI skills.

Agent Governance Toolkit

Agent governance toolkit.

Pattern Sources

Our patterns are curated from industry-leading sources with proper attribution and licensing compliance.

Refactoring.Guru

Classic GoF design patterns, code smells catalog, and refactoring techniques (https://refactoring.guru).

Enterprise Integration Patterns

65 messaging patterns for integrating enterprise applications by Gregor Hohpe and Bobby Woolf (CC BY 4.0).

Microservices.io

Comprehensive patterns for microservice architectures by Chris Richardson.

Agent Catalog Patterns

Patterns for agentic systems from agentpatternscatalog.org (CC BY 4.0).

OWASP Foundation

Security patterns from OWASP Top 10 for Web Applications, LLM Applications, and Agentic Applications (CC BY-SA 4.0).

Industry Research

ML/AI patterns from Microsoft, Google, Anthropic, and academic research.

AI Agent Patterns

Spec-driven development patterns from Claude, Gemini, OpenAI, and GitHub Copilot on github/spec-kit and OpenSpec.

Data Engineering Leaders

Data platform patterns from Martin Fowler (Data Mesh), Kimball Group (Dimensional Modeling), and cloud providers.

MLOps Best Practices

Data science patterns from MLflow, Great Expectations, and MLOps practitioners.

Streaming & Analytics

Real-time patterns from Confluent/Kafka, Apache projects, and serverless analytics platforms.

Academic Papers

Rigorous ML patterns from peer-reviewed research including data leakage prevention and active learning.

5-Day AI Agents Course

Intensive Vibe Coding Course With Google by Brenda Flynn et al. (2026) on Kaggle.

The Agent Loop

Foundational Agent Definition (Perceive + Act):
Russell, S. J., & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall. (Current edition: 4th Ed., Pearson, 2020)

Modern Iterative LLM Agent Loop:
Yao, S., Zhao, J., Yu, D., et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629.

Historical Context:
Incorporating AIMA's perceive/act model, Classical robotics' Sense-Plan-Act loop (Brooks, 1986), and ReAct's Thought→Action→Observation cycle.