Where the methodology comes from
AWS introduced the AI-Driven Development Life Cycle, and AWS Labs publishes open workflows that teams can use. The premise is straightforward: code generation is only one part of delivery. Requirements, decisions, reviews, and handoffs still need structure.
AI-DLC gives that work a defined path. Agents help progress each phase, and people approve key decisions before work moves forward.
The open methodology
Five phases, coordinated by 14 agents
- 01
Initialization
Establish the workspace, project context, and working mode.
- 02
Ideation
Clarify the intent, users, requirements, and business direction.
- 03
Inception
Shape the architecture, design, plans, and implementation approach.
- 04
Construction
Build, test, review, and secure the software.
- 05
Operation
Prepare the result for release, learning, and ongoing operation.
11 domain specialists work across product, design, architecture, development, quality, security, infrastructure, delivery, and operations; two reviewers check the work, and an adaptive composer coordinates the right workflow.
What it looks like in practice
Imagine a team adding multi-tenant support to a dashboard.
- Someone brings the idea. Maya shares the goal and current context in a shared workspace.
- The agent asks questions. It surfaces tenant isolation, existing-user, and custom-domain decisions.
- Answers become decisions. Arjun's replies are recorded as project state, not buried in chat.
- Shared artifacts take shape. A PRD and technical specification are versioned for review.
- Approved context moves to the build. A developer starts with the decisions and artifacts already attached.
- The result returns for review. The pull request and test results come back to the team.
- The next iteration keeps the context. The team can trace what changed and why.
From task assistance to lifecycle orchestration
A coding assistant waits for a prompt. An AI-DLC workflow can turn ambiguity into explicit decisions, connect those decisions to requirements and architecture, and carry the approved context into execution. If a decision changes, the team can see which downstream artifacts need another look.
Decisions
Human choices become durable project state.
Connected artifacts
PRDs, specifications, tasks, tests, and results keep their relationships.
Approval gates
People intervene where judgment, risk, and tradeoffs matter.
Persistent context
The reasoning survives beyond one prompt, tool, or chat session.
Where added structure is unnecessary
AI-DLC does not replace a team's judgment or require ceremony for every change. A small, well-understood task with clear ownership may not need a multi-stage workflow. The method is most useful when several people and agents need to share context, resolve ambiguity, or preserve a decision trail across a larger piece of work.
Two ways to use AI-DLC
SignalsAI is independent. It is not affiliated with or endorsed by AWS.