Topic
Enterprise AI
Production solutions, org knowledge, and governance for AI that a business can adopt.
4 posts.
What a Year 10 study system taught me about production AI failure modes
A personal Bedrock-adjacent build that went through three iterations and an architecture pivot. Five lessons that map directly to production AWS AI work.
Part 2: The MCP Server: Turning ADRs and Incidents into a Queryable Org-Knowledge Surface
The agent doesn't read your wiki. It calls four tools that pull frontmatter-filtered chunks out of a Bedrock Knowledge Base. Here's the contract, the code, and the small decisions that make the difference between an agent that reads your docs and one that knows your org.
Part 3: Wiring It Into AWS DevOps Agent: AgentSpace, register-service, and the IAM Trust Policy That Ate My Afternoon
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.
Part 1: Intent vs State. How AWS DevOps Agent Closes the Gap Between What Your System Is and What You Decided It Should Be
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.