Artificial Intelligence has dramatically accelerated software development. However, after using LLMs in several real-world projects, I noticed that the largest challenge was no longer generating code, it was maintaining engineering discipline. Modern software projects require much more than implementation. Every feature introduces architectural decisions, security implications, performance considerations, operational costs, and long-term maintenance challenges (to mention some). Asking a single AI assistant to handle all these concerns simultaneously often produces inconsistent results. This observation motivated me to build an AI-assisted engineering workflow that separates software development into specialized responsibilities instead of relying on one generic prompt. The Motivation One of my ongoing projects is a production-grade link curation platform lnk4st.co ( https://lnk4st.co ) inspired by services such as "Link Tree" and "link Bio". Although the platform ...