#mcp

Token optimization patterns for MCP-based agents. Quantifies upfront loading overhead and reduces token costs by 70-98% through four techniques — Progressive Discovery, tool compression proxy, Code Execution, and prompt cache alignment.

A platform approach that reduces infrastructure operational burden using Amazon Bedrock, Strands Agents SDK, and AgentCore to focus on agent development

Why DDD is an essential core in AIDLC — AI-driven development from domain design to logical design

The data foundation of AIDLC Operations — building 3-Pillar observability + AI analysis layer

Covers the 1-hour TTL constraint of EKS Kubernetes events, export pipeline design, and AI Agent query architecture based on the EKS and CloudWatch MCP servers.