#agentcore

AgentCore long-term memory has a read-after-write gotcha the docs skip: a direct BatchCreateMemoryRecords write returns 201 and stays unsearchable for 15 to 30 seconds. Measured, with the two-tier model that explains it.

The most expensive AI agent failures don't throw an error, they hide. One ran at a six-figure-a-year rate for days while every dashboard stayed green, because the signals that catch it, per-session cost and anomalies, are the ones nobody watches. Why agent loops run away, and the two cost instruments your monitoring is missing.

Strands' BedrockModel ships with prompt caching off. Two kwargs turn it on, one per-model gotcha catches you, and a 10-turn driver measures 99.9% / 99.8% hit ratios on Nova Pro and Sonnet 4.6 against an 8,156-token production system prefix. The per-call usage block proves it in seconds, not waiting on CloudWatch.

Three findings from a real customer engagement on AWS Bedrock: what a load test was actually doing, why p95 latency was 45 seconds, and the prompt-caching default that costs every team money. Plus the three CloudWatch metrics that catch all three.

A personal Bedrock-adjacent build that went through three iterations and an architecture pivot. Five lessons that map directly to production AWS AI work.

The MCP server is done. Now we plug it into AWS DevOps Agent: three CDK stacks, the AgentSpace + register-service flow, the composite-principal trust policy that you will get wrong on the first try, and a real-world OIDC gotcha that broke my own blog deploy for a month.

When something breaks at 3am, you look at logs, metrics, traces. You don't go and re-read the ADR your team wrote in January. AWS DevOps Agent does. Here's why that changes the first hour of an incident.

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.

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.

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.

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.

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.

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.