The AI-Driven Development Life Cycle — a methodology that positions AI as your central collaborator while keeping humans in control of every critical decision.
Read the Blog Post GitHub RepoAI-DLC replaces the traditional SDLC with three adaptive phases — each powered by AI execution with human oversight.
AI transforms business intent into detailed requirements through Mob Elaboration — the whole team validates AI's proposals in real time.
AI proposes architecture, domain models, code, and tests through Mob Construction — the team clarifies decisions in real time.
AI manages infrastructure-as-code and deployments with team oversight — continuous monitoring and improvement built in.
AI-DLC is built on four pillars that keep AI productive and humans in charge.
AI proposes, humans approve. Critical decisions always require human validation.
Depth and breadth of each stage adapts to the complexity of your project.
AI saves plans, requirements, and design artifacts to your repo — nothing is lost between sessions.
Replace isolated work with high-energy team sessions — AI handles routine, humans innovate.
A shift from rigid sequential phases to adaptive, AI-led workflows with human checkpoints.
| Traditional SDLC | AI-DLC | |
|---|---|---|
| Phases | 5–7 rigid stages | 3 adaptive phases |
| Planning | Human writes specs | AI drafts, humans validate |
| Coding | Human writes code | AI generates, team reviews |
| Testing | Separate QA phase | AI tests inline with code |
| Context | Docs often stale | Persistent, AI-maintained |
| Collaboration | Async handoffs | Real-time mob sessions |
Clone the open-source workflow rules, plug them into Amazon Q Developer or Kiro, and experience AI-native development.
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