Frame
Define product intent, user outcome, risk, success metric, and non-goals.
AI SDLC workflow
Use AI to build software without losing control of the lifecycle.
AIDLC Studio gives product and engineering teams a practical AI software development lifecycle: route the work, define context, generate inside boundaries, verify output, accept with evidence, release deliberately, and improve from what the team learned.
Lifecycle
The eight AIDLC workflow phases
Use this as the operating checklist before an AI-generated change reaches production.
Plan
Choose Brownfield, Greenfield, or Config/Infra route and assign human owners.
Context
Package files, APIs, tests, constraints, and known risks for the assistant.
Generate
Ask for a narrow candidate change, not an uncontrolled rewrite.
Verify
Run tests, manual checks, security review, and regression checks based on risk.
Accept
Confirm the change satisfies product acceptance criteria and engineering standards.
Release
Capture deployment, rollback, monitoring, and support notes before rollout.
Learn
Record accepted, changed, and rejected AI output so future work improves.
Workflow routes
Pick the right AI SDLC path before prompting
Brownfield Flow
Use for existing products, legacy code, production behavior, refactors, and regression-sensitive work.
- Map current behavior.
- Limit scope.
- Prove changed and unchanged behavior.
Greenfield Flow
Use for new features, services, internal tools, and MVP slices.
- Frame the outcome.
- Define architecture boundaries.
- Validate the first usable slice.
Config and Infrastructure
Use for deployment, IAM, CI/CD, cloud, config, secrets, runtime, and operational changes.
- Classify blast radius.
- Require approval gates.
- Capture rollback evidence.
FAQ
AI SDLC questions teams ask first
What is an AI SDLC workflow?
An AI SDLC workflow defines how AI-assisted work moves from request to release with scope, context, review, verification, evidence, and human ownership.
How is AI SDLC different from normal SDLC?
Normal SDLC assumes people create most candidate work. AI SDLC adds prompt contracts, assistant context boundaries, AI output review, and evidence for what the team accepted or rejected.
Can this work with any coding assistant?
Yes. The workflow is based on route, risk, context, review, and evidence rather than the model name.
Next steps