Kiro showcase: Automating Changes Across Several Repos with Spec-Driven Development and Custom Sub-Agents

As I mentioned in my previous post about engineering leadership and AI , I'm constantly looking for opportunities to experiment with AI-native development. Given how scarce hands-on time is in a manager's calendar, I'm often " vibe-coding between meetings " - trying to make the most of short, focused sessions with an AI-native SDLC. In my recent talk at AWS Community Day Slovakia , I referenced the four archetypes of AI adoption by AWS Serverless Hero Lee Gilmore. Until last year, I was a " dismissive expert " , and I still recognise that scepticism around AI in engineering almost daily - at work and online. Many sceptics limit AI to debugging tasks or use it as a glorified search engine. That's a missed opportunity. This post documents a real-world case where I used AI for a small but necessary "side-quest" that would have required several hours of tedious refactoring. It's a practical example of spec-driven development and AI-native SDLC in action. The Problem While reviewing IAM pol