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No: the SDLC is not dead. AWS’s AI-Driven Development Lifecycle (AI-DLC) is a methodology that puts AI at the center of planning and execution while people clarify intent, validate work, make critical decisions, and oversee operations. It changes who does lifecycle work and how teams organize it; it does not eliminate the lifecycle itself.
What is AI-DLC?
AI-DLC is AWS’s AI-centric software development methodology, not a standalone product. Rather than limiting AI to individual tasks or asking it to generate an entire application without human involvement, the approach uses AI across development work, with people supplying business context and retaining oversight. AWS introduced the methodology in its AI-DLC overview.
AWS describes three phases: Inception, Construction, and Operations. The phases cover familiar lifecycle activities, but shift more planning and execution to AI while keeping humans involved in decisions and review.
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How the three AI-DLC phases work
| Phase | AI’s role | People’s role |
|---|---|---|
| Inception | Turns business intent into requirements, stories, and units of work; asks clarifying questions. | Provide context, clarify intent, and validate questions and proposed work. |
| Construction | Uses validated context to propose architecture, domain models, code, and tests. | Clarify technical decisions and make architectural choices. |
| Operations | Applies context from earlier phases to infrastructure as code and deployments. | Oversee operational work and retain responsibility for critical decisions. |
AWS says plans, requirements, and design artifacts persist in the project repository so context can carry across phases. This persistence is intended to help later work draw on earlier decisions rather than treating each task as isolated.
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What changes compared with a conventional SDLC?
The useful comparison is not “lifecycle versus no lifecycle.” It is how a team distributes work and control within the lifecycle. In a conventional process, people commonly lead planning and implementation, with automation supporting selected tasks. In AI-DLC, AI takes a more central role in proposing plans and carrying out work, while humans supply context, approve or correct proposals, and remain accountable.
- Initiation and requirements: AI helps turn business intent into work proposals; people clarify what the business actually needs.
- Work size and cadence: AWS uses “Units of Work” in place of epics and calls shorter, intense cycles “bolts,” measured in hours or days rather than weeks. Those are the methodology’s organizing terms, not a promise that every team can deliver at that pace.
- Approval: Human judgment remains important for business requirements, technical choices, generated output, and operations.
- Context: Requirements and design artifacts remain in the repository to inform later phases.
- Deployment and operations: AI may help apply earlier context to infrastructure and deployments, but the team oversees the work.
One practitioner’s implementation maps familiar activities such as requirements, design, coding, testing, deployment, and monitoring to AI-DLC. That account describes one adaptation, not a universal stage count: implementations and versions can organize the work differently. See Dmitriy Trunov’s implementation account.
Does AI-DLC make the SDLC obsolete?
No. The “SDLC is dead” framing is rhetorical. AI-DLC still addresses the work associated with a software development lifecycle; it changes how that work is initiated, executed, and reviewed. AWS presents it as a new development methodology, not evidence that organizations have broadly abandoned SDLC practices.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsFor a team considering the approach, the practical question is whether AI can take on more work without weakening requirements, approval, and operational accountability. A change in execution does not remove the need to define outcomes, evaluate quality, or manage deployed software.
What AI-DLC does not guarantee
AI-generated plans, code, and tests require review. The AWS Labs AI-DLC Workflows repository warns that generative AI can make mistakes and instructs users to review generated output and costs. Generated tests do not, by themselves, prove that software is correct or safe, and human review cannot guarantee that defects will be prevented. The available descriptions also do not establish that AI-DLC makes delivery universally faster or safer.
The workflow repository provides rules for several coding-agent harnesses, including Claude Code, Kiro CLI, Kiro IDE, Codex CLI, Cursor, opencode, and GitHub Copilot. Those are implementation examples, not a requirement to use a particular tool. Repository support and instructions may change, so consult its current documentation before relying on compatibility or setup details.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When the approach may fit
AI-DLC is most relevant to teams prepared to give AI a substantial role in planning and execution while maintaining explicit human review. Before adopting it, assess whether your team can:
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- Validate requirements, architecture, code, and tests instead of treating generated work as approved by default.
- Define who makes critical decisions and who oversees deployments and operations.
- Keep project artifacts and decisions accessible across phases.
- Review both generated results and the costs of using AI tools.
If those controls are not clear, adopting an AI-centered workflow does not solve the underlying process problem; it can make mistakes harder to catch if proposals are accepted without scrutiny.
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