AI SDLC Maturity: how AI-native
is your delivery engine, really?

Ten practical questions on specs, context, architecture guardrails, implementation, UX, and QA. Get a weighted score, an honest verdict, and the first fix worth making.

0 of 10 answered

The Verdict

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0 of 10 answered

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Answer every question first. The score stays behind the curtain until there is enough signal for a useful result.

What we would actually do

Finish the assessment and we will suggest the kind of help your team probably needs next.

  • 1. Traditional
  • 2. AI-Supported
  • 3. AI-Assisted
  • 4. AI-Native
  • 5. AI-Autonomous
1. How are specifications used in your delivery process?
2. How does your team clarify requirements before coding starts?
3. How do you choose between BMAD, GSD, OpenSpec, GitHub Spec Kit, Superpowers, or similar frameworks?
4. How do you handle feature-level change specifications?
5. Where does AI get project context from?
6. How are architecture rules and project boundaries enforced?
7. How does AI participate in implementation?
8. How is UX/UI prototyping handled?
9. How do you handle QA in AI-assisted delivery?
10. How do you use knowledge graphs or dependency maps?

The maturity journey: from traditional delivery to autonomous

Where your delivery engine sits today — and what the next stage actually asks of you.

  • 1

    Traditional

    manual docs

    Work depends on people, tickets, and late clarification.

    Mostly human-only

  • 2

    AI-Supported

    prompts + trials

    AI helps individuals, but the workflow is not repeatable yet.

    Still squinting

  • 3

    AI-Assisted

    task specs + tests

    AI supports selected tasks with clearer specs and review.

    Useful structure

  • 4

    AI-Native

    living memory

    Specs, context, tests, and guardrails drive the workflow.

    Lifecycle flow

  • 5

    AI-Autonomous

    agent-owned flow

    Agents manage bounded work with tests, context, and gates.

    Agents maintain context