Field notes from teams shipping AI into production
Practical writing on agentic AI, application modernization, and product engineering. No hype, no recycled trend pieces. Just what worked, what broke, and what we would do differently next time.
Every voice agent testing platform grades how well the agent talked. A second model then decides what the conversation was worth, and when it gets that wrong, nothing alerts you.
Technical debt shows up in a scan. The debt AI-assisted development leaves in your team’s understanding does not, and it surfaces only when you need it and find it gone.
AI-generated code lands in the technical debt quadrant nobody chose. Where it forms, why review can’t catch up, and the generation-time rules that prevent it.
Reviewing AI-generated code is not one read done harder. It is five: functional, regression, security, budget, maintainability. The framework, and how to run all five without slowing merges.
The test suite passed and CI was green, yet production broke. Why AI-assisted code slips past pipelines built for human-paced change, and what verification has to become.
AI speeds up coding, not delivery. What actually gets faster, where a software estimate really goes, and how to set a timeline you can take to your board.