Part 1: Why I Chose Amazon Bedrock AgentCore (And What Lambda Gets Wrong for AI Agents)

I built a production AI agent on AWS. Not a demo, not a proof of concept — a real system with persistent memory, guardrails, CI/CD pipelines, and users who depend on it not going down at 2am. The thing nobody tells you: the hard part isn't the AI. The hard part is the infrastructure around it. This series is my attempt to document everything I had to figure out the hard way — from architecture decisions in Part 1 all the way to cost optimisation in Part 6. The companion demo repo is at github.com/rajmurugan01/bedrock-agentcore-starter. Let's start at the beginning: why Amazon Bedrock AgentCore, and why not the "obvious" serverless approach. The obvious approach: Lambda + Bedrock If you've shipped anything serverless on AWS, your first instinct is Lambda. You know it, it has great tooling, CDK support is mature, and it scales to zero. For a simple Bedrock wrapper — get a message, call InvokeModel, return a response — Lambda is fine. But the moment you add conversational state, it starts