AIDLC vs AWS AI-DLC
Same acronym, different frameworks. Two approaches share a name because the industry converged on the same problem - the SDLC needed rebuilding around AI. This page maps the differences honestly.
Same Acronym, Different Frameworks
AWS coined "AI-DLC" - the AI-Driven Development Lifecycle - in 2025 as its methodology for agentic software delivery. In August 2026 it shipped AI-DLC Workflows 2.0: native workflow tooling that runs inside the major coding agents.
The AIDLC framework documented on this site is a five-phase lifecycle developed independently around the same conviction: that the SDLC must be rebuilt around AI. Two frameworks arriving at nearly the same name is not a coincidence - it is a sign of where the whole industry is heading.
Side by Side
Both frameworks are real, current, and useful. Here is how they differ.
AIDLC (this site)
- Phases: Five - Analyze, Ideate, Develop, Launch, Curate
- Center of gravity: Methodology and mental model
- Tooling stance: Tool-agnostic - works with any assistant or agent
- Scope: The whole delivery loop, from requirements to production curation
- Origin: Independent framework documented on this site
AWS AI-DLC
- Phases: Three - Inception, Construction, Operations
- Center of gravity: Workflow tooling installed into coding agents
- Tooling stance: Ships native workflows for Kiro, Amazon Q, Cursor, Cline, Claude Code, GitHub Copilot, Codex CLI, and others
- Scope: The build loop, with session state that moves between harnesses
- Origin: Created by AWS, which cites 100+ customer experiments behind it
When to Reach for Which
They are complementary rather than competing. AWS's AI-DLC installs concrete, opinionated workflows into your coding agent - a real, working answer to "how should an agent run a delivery task?" AIDLC gives you the lifecycle mental model that organizes everything around the build loop, including the phases before a prompt is written and after the deploy lands.
In practice that means a team can adopt AWS's workflows inside Develop and Launch while using AIDLC's five phases to structure the whole - from the analysis that decides what to build to the curation that feeds production learning back into the next cycle.
Whichever shape you adopt, the underlying skill is the same: describing work clearly enough that an agent can execute it, and reviewing what comes back with judgment. That discipline is context engineering, and it pays off in either framework.
Frequently Asked Questions
No. They share an acronym but not a shape. AWS's AI-DLC is a three-phase delivery methodology - Inception, Construction, and Operations - that ships workflow tooling for coding agents. The AIDLC framework on this site is a five-phase lifecycle - Analyze, Ideate, Develop, Launch, and Curate - focused on methodology over tooling.
No. AIDLC.guru is an independent reference site written and maintained by RJ Lindelof. AWS's AI-DLC methodology and its workflow tooling are AWS's own work, and this site covers them as a related but separate approach.
Yes. They are complementary rather than competing. AWS's AI-DLC installs concrete workflows into your coding agent, while AIDLC gives you the lifecycle mental model for the whole delivery loop. Use AWS's tooling where it fits and AIDLC's five phases to organize the work around it.
One Acronym, Room for Both
Learn the five-phase framework, keep the cheatsheet handy, and borrow the best of both approaches.