AIDLC was created by RJ Lindelof, a software developer who recognized that traditional SDLC methodologies needed to evolve to embrace AI as a development partner.

Why was AIDLC created?

AIDLC was created because traditional SDLC was designed for human-only teams. With AI now capable of assisting at every phase of development, a new framework was needed to maximize the benefits of human-AI collaboration.

About AIDLC

The origins, philosophy, and vision behind the AI Development Life Cycle framework.

The Problem

For decades we've followed the same SDLC - requirements, design, develop, test, deploy, maintain. It worked, but it was built for a world where AI wasn't a collaborator.

That world is gone.

Traditional SDLC treats AI as an occasional tool, not a team member - leaving enormous potential on the table.

The Solution

AIDLC - the AI Development Life Cycle - is the framework for what comes next. Not AI replacing developers, but AI integrated at every phase, amplifying what we do.

Analyze - AI-assisted requirements, research, feasibility
Ideate - AI-driven architecture, design, documentation
Develop - AI pair programming, code generation, review
Launch - AI-generated tests, CI/CD, deployment
Curate - AI-powered monitoring, debugging, improvement

Core Philosophy

Collaboration, Not Replacement

AI is a tireless collaborator, not a replacement for human creativity and judgment. AIDLC amplifies developers.

Continuous Integration

AI woven into every phase, creating a continuous human-AI feedback loop across the lifecycle.

Tool Agnostic

A methodology, not a product. Use Claude, GPT, Gemini, or Copilot - whatever fits your team.

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Author

RJ Lindelof

Senior Engineering Leader

A 20+ year senior engineering leader recognized for driving transformative business outcomes through high-growth SaaS platforms, cloud-native migrations, and production-scale agentic AI adoption. Strongest in HealthTech and EdTech SaaS; at home in regulated B2B - most recently operationalized agentic AI across the full software lifecycle, where pilots became production infrastructure, not demos.

My strategic focus is AI-native SDLC as production infrastructure. I run a multi-model strategy across Claude Code, GitHub Copilot, AWS Kiro, OpenAI Codex, Gemini, Bolt, and Snowflake Cortex, with frontier open-weight models on vLLM for greenfield agentic platforms. 13 years as an IC before management - Java, JavaScript, Node, .NET, Delphi, C++ - and I still spin up local dev environments and prototype alongside my teams. That technical credibility is how I earn the right to push senior engineers and architects.

Player-coach means PR review, architectural review, platform modernization, and process design - not 50% feature delivery. I am past the career stage where coding tests are useful screening signal.

My AI experience is practical and production-grade - not theory, not research, not pilots that never ship. I have built and scaled AI products in production, from agentic systems and customer-facing AI features to copilots embedded in the SDLC. I make architectural decisions for complex, data-driven systems on modern cloud platforms and distributed systems, and I have the scars and the receipts to prove it.

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