Marco Mornati recounts his one-day experiment with the BMAD method (Breakthrough Method for Agile AI-Driven Development), a methodological framework that applies Agile rigor to AI-assisted development. The method relies on specialized AI personas (analyst, product manager, architect) and unfolds in five phases: market research, drafting a PRD (Product Requirements Document), breaking down into epics and stories, technical architecture, and UX design.
Mornati describes how his project's initial idea progressively strengthened and clarified over the course of the AI-guided process, notably thanks to the PRD phase. For the UX portion, he used Stitch (a Google AI tool) to avoid the generic aesthetic typical of AI-generated interfaces. For the exploratory phase, he opted for gemini-cli, reserving his more costly tokens for the actual development phase.
The central lesson of the experience is that software architecture remains the domain where human technical expertise is absolutely indispensable. AI excels at generating code, but architectural decision-making requires a deep understanding of trade-offs, system constraints, and long-term implications.
Mornati backs his reflection with market data revealing a paradox: while 93% of developers use AI assistants and 27% of code in production is now AI-generated, coding is only 26% faster and actual delivery only 8-10% faster. This gap illustrates that the bottleneck is not writing code but understanding, architecture, testing, and integration.
The article highlights the "70% problem": AI enables reaching 70% of the result quickly, but the remaining 30% - complex debugging, edge cases, system integration - requires deep human expertise. Moreover, unsupervised AI code introduces 1.7 times more defects, underscoring the critical importance of human review.
Mornati traces an evolution of the profession: from developer (one who writes code) to software engineer (one who designs systems) to "agent supervisor" (one who orchestrates AI agents). Gartner predicts that by 2028, the role will shift from implementation to orchestration. This transformation raises two major questions: how will junior developers learn their craft without hands-on coding practice, and what future awaits product managers when technical profiles can now handle the product preparation phase thanks to AI tools?