> ## Documentation Index
> Fetch the complete documentation index at: https://ainative.engineering/llms.txt
> Use this file to discover all available pages before exploring further.

# AI-Native Maturity Assessment

> Discover your AI-native engineering archetype in 3 minutes

Find out how deeply AI is integrated into your engineering practice with our interactive maturity assessment. Answer 14 behavioral questions across 7 capabilities and get your personalized results — archetype, maturity score, radar chart, and growth path.

<Card title="Take the Assessment" icon="rocket" href="https://assessment.ainative.engineering">
  14 questions. 3 minutes. Discover your AI-native engineering archetype.
</Card>

***

## The 5 Archetypes

<AccordionGroup>
  <Accordion title="The Explorer (0-20)">
    **"The AI revolution hasn't hit your workflow yet"**

    You're aware of AI coding tools and have started experimenting, but AI isn't yet a consistent part of how you work. You're in discovery mode — trying things out, seeing what sticks.

    **Growth focus:** Pick one task you do daily and try using AI for it consistently for two weeks. Build the habit before expanding scope.
  </Accordion>

  <Accordion title="The Adopter (21-40)">
    **"AI assists you, but you're still in the driver's seat"**

    AI has found a place in your workflow — you use it for specific tasks and it genuinely helps. But you're still doing most of the orchestration, context management, and quality control manually.

    **Growth focus:** Start writing structured specifications before coding. Give AI more context upfront and let it do more of the heavy lifting on implementation.
  </Accordion>

  <Accordion title="The Integrator (41-60)">
    **"AI is embedded in your workflow — you'd feel the loss"**

    AI is a core part of how you build software. You have established practices, your tools work together, and you'd significantly feel the impact if AI was removed from your workflow.

    **Growth focus:** Focus on connecting your tools and building feedback loops. Invest in context management so AI can do more with less manual setup.
  </Accordion>

  <Accordion title="The Conductor (61-80)">
    **"Your agents do the work, you set the direction"**

    You've moved beyond using AI as an assistant — you're orchestrating AI workflows, maintaining rich context, and focusing your energy on direction-setting and quality oversight.

    **Growth focus:** Build automated feedback loops and invest in observability. Let your systems learn from outcomes and self-improve.
  </Accordion>

  <Accordion title="The Architect (81-100)">
    **"You're defining how the industry builds with AI"**

    Your engineering practice is fully AI-native. Specs drive delivery, context flows seamlessly, feedback loops close automatically, and your systems continuously evolve. You're not just using AI — you're pioneering how it's used.

    **Growth focus:** Share what you've built. Write about your workflows, contribute to open-source tooling, and help others reach this level.
  </Accordion>
</AccordionGroup>

***

## How It Works

### 7 Capabilities

The assessment measures your maturity across 7 capabilities of AI-native engineering, directly from the [AI-Native Engineering Maturity Model](/resources/maturity-model):

| Capability                    | What It Measures                                                                          |
| ----------------------------- | ----------------------------------------------------------------------------------------- |
| **Spec-Driven Development**   | How you define, structure, and maintain specifications that drive AI-assisted development |
| **Context Management**        | How your AI tools access, retain, and evolve project knowledge                            |
| **Agent Collaboration**       | How AI agents participate in and coordinate across your development workflow              |
| **Observability & Feedback**  | How you measure, track, and learn from AI performance                                     |
| **Governance & Trust**        | How you ensure quality, safety, and trustworthiness of AI outputs                         |
| **Continuous Delivery**       | How connected and automated the path is from requirements to production                   |
| **Organizational Adaptation** | How your team has evolved its structure and practices around AI                           |

### Situational Judgment Format

Every question presents 5 behavioral descriptions at different maturity levels (L1-L5). You pick the one closest to your current practice — no "strongly agree/disagree" scales, no aspirational bias. Each answer IS a maturity level.

Answers are shuffled for each question so there's no pattern to game.

### Scoring

Your responses produce:

* A **score per capability** (0-100)
* An **overall maturity score** (0-100, equal-weight average of 7 capabilities)
* An **archetype** based on your overall score
* A **7-spoke radar chart** showing your profile across all capabilities
* **Growth recommendations** for your lowest-scoring capabilities

***

## Methodology

The assessment is grounded in the [AI-Native Engineering Maturity Model](/resources/maturity-model), which defines 7 capabilities across 6 maturity levels (L0-L5). Each question's answer options trace to specific behavioral descriptions in the capability matrix.

The question design was validated against 6 academic papers from arxiv.org and cross-referenced with EPAM's AI adoption survey (273 responses). No competing "developer practice maturity" assessment exists — this is the first to combine individual developer practice maturity with behavioral self-assessment.

The full question bank, scoring algorithm, and design rationale are open-source:

<Card title="Assessment Design on GitHub" icon="github" href="https://github.com/AINativeEngineering/ai-native-engineering/tree/main/assessment">
  Questions, scoring spec, and design decisions — all open for review and contribution.
</Card>