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 autocomplete, not an agent that can plan and execute work - 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 coding agents, 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 any assistant or coding agent - Claude, ChatGPT, Gemini, Cursor, Copilot - whatever fits your team.
One Acronym, Two Frameworks
AIDLC is not the only framework wearing this acronym. AWS ships its own AI-DLC - a delivery methodology with workflow tooling for coding agents. The two share an instinct, not a shape. See how they compare in AIDLC vs AWS AI-DLC.
RJ Lindelof
Senior Engineering Leader
A 20+ year senior engineering leader who drives technology strategy, platform scalability, and AI adoption. I have led engineering organizations at PE- and venture-backed companies and mid-market enterprises - always in close partnership with product - scaling multi-tenant SaaS platforms, modernizing cloud infrastructure across AWS and Azure, and taking agentic AI from pilot to production. Strongest in HealthTech and EdTech SaaS; at home in regulated B2B.
The through-line is aligning engineering execution with business strategy: embedding automation, performance, and reliability into high-growth environments so delivery accelerates, infrastructure cost comes down, and customers and investors both see measurable value. My strategic focus today is AI-native SDLC as production infrastructure - a multi-model, agent-first strategy across Claude Code, GitHub Copilot, AWS Kiro, OpenAI Codex, Gemini, Bolt, and Snowflake Cortex, increasingly wired together through MCP, with frontier open-weight models on vLLM for greenfield agentic platforms.
I hire, develop, and retain high-performing technical teams, and I have been doing it distributed since long before distributed was the default - hybrid teams since 2006, fully remote global teams since 2012. Those teams have spanned onshore, nearshore, and offshore engineers across the United States, Central America, Ukraine and the wider Eastern Europe region, Germany, India, China, and the Philippines - run as one engineering organization, not a vendor at the far end of a ticket queue.
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.
The Door Is Open
I am not actively searching, but I always keep the door open to a new opportunity - and a VP of Engineering role is the conversation I want to have next. For the right company and the right fit, let's talk.
Frequently Asked Questions
AIDLC.guru is written and maintained by RJ Lindelof, a senior engineering leader who has operationalized agentic AI across the full software lifecycle. The five-phase framework documented here is his synthesis of AI-native delivery practice.
AIDLC exists 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.
Ready to Get Started?
Explore the framework and start implementing AI-Native Software Delivery today.