Add agentic AI methodology documentation for AWS GenAI Competency

- Add Agentic AI Implementation Methodology doc covering requirements analysis, model selection, and framework choices
- Add Agent Security & Interoperability doc covering authentication, authorization, and interaction protocols
- Add Agent Compute Deployment doc covering AWS managed compute services for agent workloads
- Update mkdocs.yml navigation to include new documents

Author: marceloacosta

docs/genai/agent-compute-deployment.md

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+# Agent Compute Deployment
+
+## Overview
+
+Effective agent deployment requires selecting appropriate AWS managed compute services that balance security, cost efficiency, and operational simplicity. This document outlines our methodology for deploying agent workloads on AWS managed compute infrastructure, demonstrating expertise in service selection and optimization strategies.
+
+## Compute Service Selection Framework
+
+### Evaluation Criteria
+
+Service selection considers multiple dimensions:
+
+**Workload Characteristics:**
+
+- Request patterns (synchronous vs. asynchronous)
+- Latency requirements (real-time vs. batch)
+- Execution duration (seconds vs. minutes vs. hours)
+- Memory and CPU requirements
+- Concurrency expectations
+
+**Operational Requirements:**
+
+- Scalability needs (auto-scaling, burst capacity)
+- Availability requirements (multi-AZ, regional)
+- Deployment frequency and complexity
+- Monitoring and observability needs
+- Cost optimization priorities
+
+**Security Posture:**
+
+- Network isolation requirements
+- IAM integration complexity
+- Secrets management approach
+- Compliance and audit requirements
+
+## AWS Managed Compute Options
+
+### Amazon Bedrock AgentCore Runtime
+
+**Recommended for:** Production agentic AI workloads requiring managed infrastructure
+
+Amazon Bedrock AgentCore Runtime provides a fully managed execution environment for agents:
+
+**Capabilities:**
+
+- Secure serverless agent hosting
+- Automatic scaling based on demand
+- Built-in session management
+- Native Bedrock model integration
+- AgentCore Gateway for tool orchestration
+- AgentCore Memory for conversation persistence
+
+**Architecture Pattern:**
+
+```
+Client Request → API Gateway → AgentCore Runtime →
+├─ Agent Execution (Strands/Bedrock Agents)
+├─ Tool Invocation via AgentCore Gateway
+├─ Memory Access via AgentCore Memory
+└─ Model Inference via Amazon Bedrock
+→ Response Streaming → Client
+```
+
+**Security Features:**
+
+- VPC integration for network isolation
+- IAM-based authentication
+- Encryption at rest and in transit
+- CloudTrail audit logging
+
+**Cost Model:**
+
+- Pay-per-invocation pricing
+- No idle capacity charges
+- Included scaling and management
+
+### AWS Lambda
+
+**Recommended for:** Event-driven agent tools, lightweight orchestration, API backends
+
+Lambda provides serverless compute for agent components:
+
+**Use Cases:**
+
+- Agent tool implementations
+- API backend handlers
+- Event processing (S3, DynamoDB streams)
+- Scheduled agent tasks
+
+**Configuration Recommendations:**
+
+- Memory allocation based on workload profiling
+- Timeout settings aligned with SLAs
+- Provisioned concurrency for latency-sensitive paths
+- Layer usage for shared dependencies
+
+**Architecture Pattern:**
+
+```
+Event Source → Lambda Function →
+├─ Business Logic Execution
+├─ AWS Service Integration
+└─ External API Calls
+→ Response/Next Action
+```
+
+**Optimization Strategies:**
+
+- Right-size memory allocation (128MB - 10GB)
+- Connection pooling for database access
+- Async invocation for non-blocking operations
+- Reserved concurrency for critical functions
+
+### Amazon ECS with Fargate
+
+**Recommended for:** Long-running agent processes, container-based deployments
+
+ECS Fargate provides serverless container orchestration:
+
+**Use Cases:**
+
+- Persistent agent services
+- Batch processing workloads
+- Custom runtime requirements
+- Complex dependency management
+
+**Architecture Pattern:**
+
+```
+Load Balancer → ECS Service (Fargate) →
+├─ Container Task Execution
+├─ Service Discovery Integration
+└─ Auto-scaling based on metrics
+→ Response
+```
+
+**Configuration Recommendations:**
+
+- Task CPU and memory sizing based on profiling
+- Service auto-scaling policies (target tracking, step scaling)
+- Health check configuration for reliability
+- Log driver configuration for CloudWatch integration
+
+**Security Configuration:**
+
+- Task execution role with minimal permissions
+- Task role for application permissions
+- Security groups for network access control
+- Secrets injection from Secrets Manager
+
+### Amazon EKS
+
+**Recommended for:** Complex multi-agent systems, Kubernetes-native organizations
+
+EKS provides managed Kubernetes for sophisticated deployments:
+
+**Use Cases:**
+
+- Multi-agent orchestration platforms
+- Hybrid cloud deployments
+- Teams with Kubernetes expertise
+- Complex networking requirements
+
+**Architecture Pattern:**
+
+```
+Ingress Controller → Kubernetes Service →
+├─ Pod Deployment (agent containers)
+├─ Horizontal Pod Autoscaler
+├─ Service Mesh (optional)
+└─ Persistent Volume Claims
+→ Response
+```
+
+**Operational Considerations:**
+
+- Node group sizing and instance selection
+- Cluster autoscaler configuration
+- Add-on management (CoreDNS, kube-proxy, VPC CNI)
+- Monitoring via Container Insights
+
+## Deployment Strategies
+
+### Blue/Green Deployment
+
+Zero-downtime deployments with instant rollback:
+
+```
+Production (Blue) ← Traffic
+├─ Deploy to Green environment
+├─ Validate Green health
+├─ Switch traffic Blue → Green
+└─ Retain Blue for rollback
+New Production (Green) ← Traffic
+```
+
+**Implementation:**
+
+- CodeDeploy for Lambda and ECS
+- Route 53 weighted routing for gradual shift
+- ALB target group switching for instant cutover
+
+### Canary Deployment
+
+Gradual traffic shift with monitoring:
+
+```
+Production ← 95% Traffic
+Canary ← 5% Traffic
+├─ Monitor error rates, latency
+├─ Gradually increase canary percentage
+└─ Full promotion or rollback
+```
+
+**Implementation:**
+
+- Lambda aliases with weighted routing
+- ECS service with multiple task definitions
+- CloudWatch alarms for automatic rollback
+
+### Rolling Deployment
+
+Incremental update of running instances:
+
+```
+[v1] [v1] [v1] [v1] ← Initial state
+[v2] [v1] [v1] [v1] ← First batch
+[v2] [v2] [v1] [v1] ← Second batch
+[v2] [v2] [v2] [v2] ← Complete
+```
+
+**Implementation:**
+
+- ECS rolling update configuration
+- EKS rolling deployment strategy
+- Health check validation between batches
+
+## Cost Optimization
+
+### Right-Sizing
+
+Continuous optimization of resource allocation:
+
+- Lambda memory profiling with AWS Lambda Power Tuning
+- ECS task size analysis with Container Insights
+- Compute Optimizer recommendations review
+
+### Reserved Capacity
+
+Cost reduction for predictable workloads:
+
+- Savings Plans for Lambda and Fargate
+- Reserved Instances for EKS node groups
+- Commitment analysis based on historical usage
+
+### Spot Integration
+
+Cost optimization for fault-tolerant workloads:
+
+- ECS Fargate Spot for batch processing
+- EKS Spot node groups for non-critical workloads
+- Graceful handling of Spot interruptions
+
+## Security Posture
+
+### Network Security
+
+- VPC deployment with private subnets
+- VPC endpoints for AWS service access
+- Security groups with minimal ingress rules
+- Network ACLs for subnet-level control
+
+### Identity and Access
+
+- Execution roles with least privilege
+- Task/pod identity for application permissions
+- Secrets injection (never environment variables)
+- Credential rotation automation
+
+### Encryption
+
+- In-transit encryption (TLS 1.2+)
+- At-rest encryption for all storage
+- KMS key management with rotation
+- Client-side encryption where appropriate
+
+## Monitoring and Observability
+
+### Metrics Collection
+
+- CloudWatch Container Insights for ECS/EKS
+- Lambda Insights for function telemetry
+- Custom metrics for business KPIs
+- X-Ray tracing for distributed operations
+
+### Alerting Configuration
+
+- Latency threshold alerts
+- Error rate monitoring
+- Resource utilization warnings
+- Cost anomaly detection
+
+### Dashboard Standards
+
+Standard dashboards for each compute service:
+
+- Request volume and patterns
+- Latency percentiles (p50, p95, p99)
+- Error rates and types
+- Resource utilization trends
+- Cost tracking and forecasting