Blog
Deep dives into building production AI systems on AWS. No fluff — just real code, real gotchas, real architecture decisions.
Ultimate Guide to Building AI Agents on AWS with Bedrock AgentCore
Part 6: Cost & Performance for Bedrock AgentCore — Prompt Caching, Model Selection, and CloudWatch Alarms
Real cost breakdown of running an AgentCore agent: prompt caching savings, when to use Nova Pro vs Claude Sonnet, PriceClass_100, idle timeouts, and how to set alarms before your bill surprises you.
Part 5: CI/CD for Bedrock AgentCore with GitHub Actions and AWS OIDC (No Stored Credentials)
How to build a complete CI/CD pipeline for AgentCore using GitHub Actions OIDC: no stored AWS keys, dual-tag ECR strategy, automated Runtime updates, and multi-environment promotion.
Part 4: Running Your AgentCore Agent Locally with Docker (The Right Way)
How to build and run your AgentCore container locally with real AWS credentials, the correct linux/amd64 platform flag, the .env.local pattern, and how to test with curl.
Part 3: Building the AI Agent with Strands Agents SDK, Prompt Caching, and AgentCore Memory
How to build the Python agent that runs inside AgentCore: Strands SDK setup, prompt caching that cuts costs by 90%, dual-model strategy, tool definitions, and AgentCore Memory integration.
Part 2: CDK Infrastructure for Amazon Bedrock AgentCore (And Every Gotcha You'll Hit)
A complete CDK v2 TypeScript stack for Bedrock AgentCore — with inline comments for every deployment trap: naming constraints, ECR bootstrap, missing L1 constructs, VPC endpoint conflicts, and more.
Part 1: Why I Chose Amazon Bedrock AgentCore (And What Lambda Gets Wrong for AI Agents)
Before writing a single line of agent code, I spent a week figuring out where to run it. Here's the architecture decision that changed everything — and the Lambda limitations that forced my hand.