AI-DLC – Introducing AI Driven Development Lifecycle

AI-DLC (AI-Driven Development Lifecycle) is a transformative methodology introduced by AWS in 2025, reimagining software engineering by positioning AI as a central collaborator throughout the entire development process.

The AI-Driven Development Life Cycle (AI-DLC) represents a transformative approach to software engineering that positions AI as a central collaborator throughout the development process.

Unlike traditional AI-assisted development methods, AI-DLC reimagines the entire software lifecycle through two key dimensions: AI-powered execution with human oversight and dynamic team collaboration.

The methodology operates in three phases – Inception, Construction, and Operations – where AI initiates workflows while maintaining persistent context across all stages. This innovative approach delivers significant benefits including increased velocity, enhanced quality, improved market responsiveness, and better developer experience, all while ensuring critical human oversight and collaboration.

Unlike traditional SDLC (where AI assists specific tasks) or fully autonomous approaches, AI-DLC emphasizes two core dimensions:

• AI-Powered Execution with Human Oversight — AI initiates workflows, creates plans, generates code, tests, and iterates, while actively seeking clarification and deferring key decisions to humans.
• Dynamic Team Collaboration — AI handles routine work, freeing teams for creative problem-solving, alignment, and real-time decision-making in collaborative spaces.

The methodology structures development into three adaptive phases:

• Inception — Planning, requirements gathering, architecture, and design (AI proposes and refines based on context).
• Construction — Detailed design, implementation, testing, and iteration.
• Operations — Deployment, monitoring, observability, and ongoing maintenance.

AI maintains persistent semantic-rich context across phases, automatically detects workspaces, reverse-engineers needs, and adapts workflows to project complexity (e.g., skipping heavy planning for simple fixes).

Tools like Amazon Q Developer, Kiro, and open-source adaptive workflows (e.g., on GitHub) implement AI-DLC principles, enabling faster velocity, higher quality, better market responsiveness, and improved developer experience—while preserving engineering discipline and human judgment.

As of 2026, AI-DLC represents a shift toward AI-native software development, moving beyond assistants to true human-AI teaming.