Each phase runs short iterations where all disciplines work together, not in sequence. Phases overlap throughout the project lifecycle.
One diagram showing how the four phases interlock, from initial requirements through to production.
The same methodology adapts to two realities, a clean slate, or an existing system you can't break.
A Requirements Engineer creates use cases and an entity model. The AI agent generates code and tests directly from those artifacts. The Software Engineer reviews, every artifact traces back to a requirement.
Start from the running system. The Software Engineer reverse-engineers the entity model, use case model, and specifications from the existing code. Software Engineer and Requirements Engineer then review those artifacts together, establishing the spec baseline that future iterations build on.
Principles that ensure success in agile, iterative development.
Specifications drive everything else, not code.
AI handles tedious work; humans focus on business logic.
Specs, code, and tests evolve together through short cycles.
Comprehensive tests ensure consistent behavior during AI regeneration.
Continuous validation with business users at every iteration.
Every line of code traces back to a business requirement.
It's not about perfect specs, it's about iterative improvement.
Critics argue AI code generation only works with exhaustive specifications that force deterministic output. This assumes we need perfect requirements upfront.
Reality: Perfect specifications are impossible and unnecessary. The real value comes from iterative improvement.
Through short cycles, specifications become clearer, AI generation improves, and tests get stronger. Each iteration builds on the previous one.
Key insight: Tests ensure consistent behavior regardless of how the AI generates code. This enables safe evolution and modernization.
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