Deploying On Azure Agent Skill

Azure Patterns

Design and implement Azure cloud architectures following Microsoft's Well-Architected Framework and best practices for service selection, cost optimization, and security.

When to Use

Use this skill when:

• Designing new applications for Azure cloud
• Selecting Azure compute services (Container Apps, AKS, Functions, App Service)
• Architecting storage solutions (Blob Storage, Files, Cosmos DB)
• Integrating Azure OpenAI or Cognitive Services
• Implementing messaging patterns (Service Bus, Event Grid, Event Hubs)
• Designing secure networks with Private Endpoints
• Applying Azure governance and compliance policies
• Optimizing Azure costs and performance

Core Concepts

Service Selection Philosophy

Azure offers 200+ services. Choose based on:

• Managed vs. IaaS - Prefer fully managed services (lower operational burden)
• Cost Model - Consumption vs. dedicated capacity
• Integration Requirements - Microsoft 365, Active Directory, hybrid cloud
• Control vs. Simplicity - More control = more operational overhead

Azure Well-Architected Framework (Five Pillars)

| Pillar | Focus | Key Practices |

|--------|-------|---------------|

| Cost Optimization | Maximize value within budget | Reserved Instances, auto-scaling, lifecycle management |

| Operational Excellence | Run reliable systems | Azure Policy, automation, monitoring |

| Performance Efficiency | Scale to meet demand | Autoscaling, caching, CDN |

| Reliability | Recover from failures | Availability Zones, multi-region, backup |

| Security | Protect data and assets | Managed Identity, Private Endpoints, Key Vault |

Reference references/well-architected.md for detailed pillar implementation patterns.

Compute Service Selection

Decision Framework

Container-based workload?
  YES → Need Kubernetes control plane?
          YES → Azure Kubernetes Service (AKS)
          NO → Azure Container Apps (recommended)
  NO → Event-driven function?
         YES → Azure Functions
         NO → Web application?
                YES → Azure App Service
                NO → Legacy/specialized → Virtual Machines

Service Comparison

| Service | Best For | Pricing Model | Operational Overhead |

|---------|----------|---------------|---------------------|

| Container Apps | Microservices, APIs, background jobs | Consumption or dedicated | Low |

| AKS | Complex K8s workloads, service mesh | Node-based | High |

| Functions | Event-driven, short tasks (<10 min) | Consumption or premium | Low |

| App Service | Web apps, simple APIs | Dedicated plans | Low |

| Virtual Machines | Legacy apps, specialized software | VM-based | High |

Recommendation: Start with Azure Container Apps for 80% of containerized workloads (simpler and cheaper than AKS).

Reference references/compute-services.md for detailed comparison with Bicep and Terraform examples.

Storage Architecture

Blob Storage Tier Selection

| Tier | Access Pattern | Cost/GB/Month | Minimum Storage Duration |

|------|---------------|---------------|--------------------------|

| Hot | Daily access | $0.018 | None |

| Cool | <1/month access | $0.010 | 30 days |

| Cold | <90 days access | $0.0045 | 90 days |

| Archive | Rare access | $0.00099 | 180 days |

Pattern: Use lifecycle management policies to automatically move data to lower-cost tiers.

Storage Service Decision

File system interface required?
  YES → Protocol?
          SMB → Azure Files (or NetApp Files for high performance)
          NFS → Azure Files (NFS 4.1)
  NO → Object storage → Blob Storage
       Block storage → Managed Disks (Standard/Premium SSD/Ultra)
       Analytics → Data Lake Storage Gen2

Reference references/storage-patterns.md for lifecycle policies, redundancy options, and performance tuning.

Database Service Selection

Decision Framework

Relational data?
  YES → SQL Server compatible?
          YES → Need VM-level access?
                  YES → SQL Managed Instance
                  NO → Azure SQL Database
          NO → Open source?
                 PostgreSQL → PostgreSQL Flexible Server
                 MySQL → MySQL Flexible Server
  NO → Data model?
         Document/JSON → Cosmos DB (NoSQL API)
         Graph → Cosmos DB (Gremlin API)
         Wide-column → Cosmos DB (Cassandra API)
         Key-value cache → Azure Cache for Redis
         Time-series → Azure Data Explorer

Cosmos DB Consistency Levels

| Level | Use Case | Latency | Throughput |

|-------|----------|---------|------------|

| Strong | Financial transactions, inventory | Highest | Lowest |

| Bounded Staleness | Real-time leaderboards with acceptable lag | High | Low |

| Session | Shopping carts, user sessions (default) | Medium | Medium |

| Consistent Prefix | Social feeds, IoT telemetry | Low | High |

| Eventual | Analytics, ML training data | Lowest | Highest |

Reference references/database-selection.md for capacity planning, indexing strategies, and migration patterns.

AI and Machine Learning Integration

Azure OpenAI Service

Use Cases:

• Chatbots and conversational AI (GPT-4)
• Content generation and summarization
• Semantic search with embeddings (RAG pattern)
• Code generation and completion
• Function calling for structured outputs

Key Advantages:

• Enterprise data privacy (no model training on customer data)
• Regional deployment for data residency
• Microsoft enterprise SLAs
• Built-in content filtering

Integration Pattern:

from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()
client = AzureOpenAI(
    azure_endpoint="https://myopenai.openai.azure.com",
    azure_ad_token_provider=token_provider,
    api_version="2024-02-15-preview"
)

response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

Other AI Services

| Service | Purpose | Common Use Cases |

|---------|---------|------------------|

| Cognitive Services | Pre-built AI models | Vision, Speech, Language, Decision |

| Azure Machine Learning | Custom model training | MLOps, model deployment, feature engineering |

| Azure AI Search | Semantic search engine | RAG patterns, document search |

Reference references/ai-integration.md for RAG architecture, function calling, and fine-tuning patterns.

Messaging and Integration

Service Selection Matrix

| Service | Pattern | Message Size | Ordering | Transactions | Best For |

|---------|---------|--------------|----------|--------------|----------|

| Service Bus | Queue/Topic | 256 KB - 100 MB | Yes (sessions) | Yes | Enterprise messaging |

| Event Grid | Pub/Sub | 1 MB | No | No | Event-driven architectures |

| Event Hubs | Streaming | 1 MB | Yes (partitions) | No | Big data ingestion, telemetry |

| Storage Queues | Simple queue | 64 KB | No | No | Async work, <500k msgs/sec |

When to Use What:

• Service Bus: Reliable messaging with transactions (e.g., order processing)
• Event Grid: React to Azure resource events (e.g., blob created, VM stopped)
• Event Hubs: High-throughput streaming (e.g., IoT telemetry, application logs)

Reference references/messaging-patterns.md for implementation examples, retry policies, and dead-letter handling.

Networking Architecture

Private Endpoints vs. Service Endpoints

| Aspect | Private Endpoint | Service Endpoint |

|--------|------------------|------------------|

| Security Model | Private IP in VNet | Optimized route to public endpoint |

| Data Exfiltration Protection | Yes (network-isolated) | Limited (service firewall only) |

| Cost | ~$7.30/month per endpoint | Free |

| Recommendation | Production workloads | Dev/test environments |

Best Practice: Use Private Endpoints for all PaaS services in production (treat public endpoints as anti-pattern).

Hub-and-Spoke Topology

Components:

• Hub VNet: Shared services (Azure Firewall, VPN Gateway, Private Endpoints)
• Spoke VNets: Application workloads (isolated per environment or team)
• VNet Peering: Low-latency connectivity between hub and spokes

Benefits:

• Centralized security (firewall, DNS)
• Cost optimization (shared egress)
• Simplified governance

Reference references/networking-architecture.md for hub-spoke Bicep templates, NSG patterns, and DNS configuration.

Identity and Access Management

Managed Identity Pattern

Always use Managed Identity instead of:

• Connection strings in code
• Storage account keys
• Service principal credentials
• API keys

System-Assigned vs. User-Assigned:

| Type | Lifecycle | Use Case |

|------|-----------|----------|

| System-Assigned | Tied to resource | Single resource needs access |

| User-Assigned | Independent | Multiple resources share identity |

Example Flow:

• Enable Managed Identity on Container App
• Grant identity access to Key Vault (RBAC or Access Policy)
• Application authenticates automatically (no credentials)

from azure.identity import DefaultAzureCredential

# Works automatically with Managed Identity
credential = DefaultAzureCredential()
keyvault_client = SecretClient(vault_url="...", credential=credential)

Azure RBAC Best Practices

• Use built-in roles when possible (Owner, Contributor, Reader)
• Apply least privilege principle
• Assign roles at resource group level (not subscription)
• Use Azure AD groups for user management
• Audit role assignments regularly

Reference references/identity-access.md for Entra ID integration, Conditional Access policies, and B2C patterns.

Governance and Compliance

Azure Policy for Guardrails

Common Policy Patterns:

• Require tags on all resources (Environment, Owner, CostCenter)
• Restrict allowed Azure regions
• Enforce TLS 1.2 minimum
• Require Private Endpoints for storage accounts
• Deny public IP addresses on VMs

Policy Effects:

• Deny: Block non-compliant resource creation
• Audit: Log non-compliance but allow creation
• DeployIfNotExists: Auto-remediate missing configurations
• Modify: Change resource properties during deployment

Cost Management

Optimization Strategies:

| Pattern | Savings | Use Case |

|---------|---------|----------|

| Reserved Instances (1-year) | 40-50% | Steady-state workloads (databases, VMs) |

| Reserved Instances (3-year) | 60-70% | Long-term commitments |

| Spot VMs | Up to 90% | Fault-tolerant batch processing |

| Auto-shutdown | Variable | Dev/test resources (off-hours) |

| Storage lifecycle policies | 50-90% | Move to Cool/Archive tiers |

Monitoring:

• Set budgets and alerts in Azure Cost Management
• Review Azure Advisor cost recommendations weekly
• Tag resources for cost allocation
• Use FinOps Toolkit for Power BI dashboards

Reference references/governance-compliance.md for Azure Landing Zones, Policy definitions, and Blueprints.

Infrastructure as Code

Tool Selection

| Tool | Best For | Azure Integration | Multi-Cloud |

|------|----------|-------------------|-------------|

| Bicep | Azure-native projects | Excellent (official) | No |

| Terraform | Multi-cloud environments | Good (azurerm provider) | Yes |

| Pulumi | Developer-first approach | Good (native SDK) | Yes |

| Azure CLI | Scripts and automation | Excellent | No |

Recommendation:

• Use Bicep for Azure-only infrastructure (best Azure integration, native type safety)
• Use Terraform for multi-cloud or existing Terraform shops
• Use Azure CLI for quick scripts and CI/CD automation

Bicep Best Practices

• Use parameter files for environment-specific values
• Leverage Azure Verified Modules (AVM) for tested patterns
• Organize by resource lifecycle (networking, data, compute)
• Use symbolic names (not string interpolation)
• Enable linting and validation in CI/CD

Reference Bicep and Terraform examples in examples/bicep/ and examples/terraform/ directories.

Security Best Practices

Essential Security Controls

| Control | Implementation | Priority |

|---------|---------------|----------|

| Managed Identity | Enable on all compute resources | Critical |

| Private Endpoints | All PaaS services in production | Critical |

| Key Vault | Store secrets, keys, certificates | Critical |

| Network Segmentation | NSGs, application security groups | High |

| Microsoft Defender | Enable for all resource types | High |

| Azure Policy | Preventive controls | High |

| Just-In-Time Access | VMs and privileged access | Medium |

Defense-in-Depth Layers

• Network: Private Endpoints, NSGs, Azure Firewall
• Identity: Entra ID, Managed Identity, Conditional Access
• Application: Web Application Firewall, API Management
• Data: Encryption at rest, encryption in transit (TLS 1.2+)
• Monitoring: Microsoft Defender, Azure Monitor, Sentinel

Reference references/security-architecture.md (see also security-hardening and auth-security skills).

Cost Estimation

Pricing Considerations

Compute:

• Container Apps: ~$60/month (1 vCPU, 2GB RAM, 24/7)
• AKS: ~$400/month (3-node D4s_v5 cluster)
• App Service P1v3: ~$145/month (2 vCPU, 8GB RAM)
• Functions Consumption: ~$0.20 per 1M executions

Storage:

• Blob Hot: $0.018/GB/month
• Blob Cool: $0.010/GB/month
• Blob Archive: $0.00099/GB/month
• Managed Disks Premium SSD: $0.15/GB/month

Database:

• Azure SQL Database (2 vCores): ~$280/month
• Cosmos DB Serverless: Pay per RU consumed
• PostgreSQL Flexible (2 vCores): ~$125/month

Use Azure Pricing Calculator: https://azure.microsoft.com/pricing/calculator/

Quick Reference Tables

Compute Service Decision Matrix

| If You Need... | Choose |

|----------------|--------|

| Kubernetes features (CRDs, operators) | Azure Kubernetes Service |

| Microservices without K8s complexity | Azure Container Apps |

| Event-driven functions (<10 min) | Azure Functions |

| Traditional web app (Node, .NET, Python) | Azure App Service |

| Batch processing, HPC | Azure Batch or VM Scale Sets |

| Legacy application migration | Virtual Machines |

Storage Service Decision Matrix

| If You Need... | Choose |

|----------------|--------|

| SMB file shares | Azure Files |

| NFS file shares | Azure Files (NFS 4.1) |

| Object storage (images, backups) | Blob Storage |

| High-performance file storage | Azure NetApp Files |

| Block storage for VMs | Managed Disks |

| Big data analytics | Data Lake Storage Gen2 |

Database Service Decision Matrix

| If You Need... | Choose |

|----------------|--------|

| SQL Server features (T-SQL, SQL Agent) | Azure SQL Database or Managed Instance |

| PostgreSQL | PostgreSQL Flexible Server |

| MySQL | MySQL Flexible Server |

| Global distribution, multi-model | Cosmos DB |

| In-memory cache | Azure Cache for Redis |

| Graph database | Cosmos DB (Gremlin API) |

| Time-series data | Azure Data Explorer |

Integration with Other Skills

• infrastructure-as-code: Implement Azure patterns using Bicep or Terraform
• kubernetes-operations: AKS-specific configuration and operations
• deploying-applications: Container Apps and App Service deployment
• building-ci-pipelines: Azure DevOps and GitHub Actions integration
• auth-security: Entra ID authentication and authorization patterns
• observability: Azure Monitor and Application Insights
• ai-chat: Azure OpenAI Service for chat applications
• databases-nosql: Cosmos DB implementation details
• secret-management: Azure Key Vault integration patterns

Reference Documentation

For detailed implementation guidance, see:

• references/compute-services.md - Container Apps, AKS, Functions, App Service with Bicep/Terraform
• references/storage-patterns.md - Blob Storage, Files, Disks, lifecycle management
• references/database-selection.md - SQL Database, Cosmos DB, PostgreSQL patterns
• references/ai-integration.md - Azure OpenAI, RAG architecture, function calling
• references/messaging-patterns.md - Service Bus, Event Grid, Event Hubs examples
• references/networking-architecture.md - Hub-spoke, Private Endpoints, DNS configuration
• references/identity-access.md - Entra ID, Managed Identity, RBAC
• references/governance-compliance.md - Azure Policy, Landing Zones, cost optimization
• references/well-architected.md - Five pillars implementation guide

Code Examples

Working examples available in:

• examples/bicep/ - Infrastructure templates (Container Apps, AKS, networking, databases)
• examples/terraform/ - Multi-cloud IaC examples
• examples/sdk/python/ - Python SDK integration (OpenAI, Managed Identity, messaging)
• examples/sdk/typescript/ - TypeScript SDK examples

Additional Resources

• Azure Architecture Center: https://learn.microsoft.com/azure/architecture/
• Azure Well-Architected Framework: https://learn.microsoft.com/azure/well-architected/
• Azure Verified Modules: https://aka.ms/avm
• Azure Charts (Service Comparison): https://azurecharts.com/
• Azure Updates: https://azure.microsoft.com/updates/