Architecting AI-SDLC: Five Lessons For DevSecFinOps Teams
Every morning, when we open a newspaper or scroll through a news app, there's another CEO talking nonstop about how they've "solved coding." That might work for new-age startups, but when you try to replace the legacy Software Development Life Cycle (SDLC) with AI inside an enterprise, it turns out to be a real challenge. You're not bottlenecked by typing speed anymore. Instead, you're suddenly fighting to preserve context, keep your cloud bills from bankrupting the company, and stop autonomous agents from doing whatever they want. If you've ever tried using AI on large-scale distributed systems, you know that it completely rewires your architecture and your engineering culture.
1. Cognitive Debt Replaces Technical Debt
We were pretty excited at first that tools were pushing out massive chunks of boilerplate in seconds. Then a much uglier problem surfaced: cognitive debt. An agent drops hundreds of lines of code into your IDE,...
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