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 Life Cycle - in 2025 as its methodology for agentic software delivery, first described as three phases. What it ships today is broader: AI-DLC Workflows reached version 2.9.0 in September 2026, and its documentation now describes five phases across 33 stages, installed as a native command that runs inside seven 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: Five - Initialization, Ideation, Inception, Construction, Operation - broken into 33 stages
- Center of gravity: Workflow tooling installed into coding agents
- Tooling stance: One harness-neutral core that runs in Claude Code, Kiro CLI, Kiro IDE, Codex CLI, Cursor, opencode, and GitHub Copilot
- Scope: The whole lifecycle in 33 stages, with per-intent state, approval gates, and an audit trail
- Origin: Created by AWS, which cites 100+ customer experiments behind it
One Core, Many Harnesses
"One core, many harnesses" is how AWS now describes AI-DLC: a single harness-neutral engine that installs into whichever coding agent you already run. You install a native command, point a project at one harness, and start work by describing it. The methodology is identical everywhere; what differs is the shell around it.
What arrives with it is prescriptive. AWS documents five phases and 33 stages, a set of agent personas, workflow profiles that range from a short bug fix to a full enterprise run, human approval gates at stage boundaries, and an audit trail. That is a different kind of artifact from a lifecycle model - it is an engine with opinions about your next move, which is exactly its appeal and exactly its cost.
Two things are worth knowing before you plan around it. The harness is a per-project choice rather than a portable session, so work does not simply follow you from one agent to another. And Amazon Q Developer is no longer on the list: AWS has announced end of support for its IDE plugins and paid subscriptions in April 2027 and points those customers at Kiro instead.
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.
The overlap is wider than it used to be. AWS's stages now reach back into research and feasibility and forward into deployment and feedback, so this is no longer a clean division of labor. A team can still adopt AWS's workflows where they bite hardest - 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 delivery methodology whose shipped workflows document five phases across 33 stages - Initialization, Ideation, Inception, Construction, and Operation - installed as an engine inside your coding agent. 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.