Design decisions behind large-scale production systems in Japan. I write about the objective each choice was serving, the trade-off it accepted, how the conditions moved afterwards, and what turned out to be optimal when. Cloud architecture, distributed systems, AI in production, and the organizational choices that shape how systems evolve. I write down the conditions each decision depended on, so you can tell when it no longer holds.
AI-driven development does make much of the development process faster. It does not remove the constraint, the bottleneck in that process. The constraint moves instead, and where it lands shows whethe
Same workload, same tools, one model swapped: the cost per request moves by 96x. Somewhere in that range sits a budget your business has already decided, and you can compute it before you write a line
The AI era changes exactly one thing about architecture. The component at the center of your system is now probabilistic. Everything else, the discipline of starting from the problem, naming constrain
A proof of concept that passed in 2022 failed its load test in 2025. Nothing in the technology had regressed. The acceptance criteria had grown up. The project behind that sentence is one of the large
On stage, you declare a design correct. In production, you find out what that correctness was quietly resting on. In 2022, at the first Architecture Dojo at AWS Summit Japan, I stood on stage and led
An AI coding agent does not bend a productivity curve on its own. A team does. Coding agents, models, and IDE plugins ship new versions every few months, and most of what gets called "best practice" i
Most teams pick the AI agent evaluation tool their framework integrates with, then a quarter later notice that one corner of the evaluation space is covered and the rest is exposed. The market has no
Some incidents look minor on paper. A small single-digit percentage of instances affected, in a single AZ. And yet the user-visible outcome is that more than half of the service's transactions stop wo
Your system can handle 10,000 requests per second. But can it handle going from zero to 10,000 in one second? Peak traffic forces a design choice: what do you include in your scaling scope? Compute, d
