agency-ladder

Agency Ladder - AI Autonomy Progression Planning

Core Philosophy

AI products earn autonomy. They don't start with it.

Every increase in AI agency means surrendering human control. This tradeoff must be intentional, not accidental.

Low Agency ←──────────────────→ High Agency
(Human decides)              (AI decides)

High Control ←──────────────→ Low Control
(Predictable)                (Unpredictable)

Key insight: You're not ready to give high agency until you've thoroughly tested how the AI behaves at lower autonomy levels.

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Entry Point

When this skill is invoked, start with:

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 AGENCY LADDER
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Every AI feature should earn autonomy, not start with it.

What are you working on?

  1. Planning a new AI feature
     → Build the ladder from scratch

  2. Reviewing an existing AI feature
     → Map current state, plan next level

  3. Deciding whether to increase agency
     → Promotion criteria check

  4. Workshop/conversation tool
     → Export ladder for stakeholder discussion

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Parse intent from context:

• If user mentions "new feature" or "planning" → Flow 1
• If user mentions "existing" or "current" → Flow 2
• If user mentions "promote" or "increase" or "ready" → Flow 3
• If user mentions "stakeholder" or "present" or "export" → Flow 4

Command-line shortcuts:

• /agency-ladder → Show entry menu
• /agency-ladder --review → Flow 2 (existing feature)
• /agency-ladder --promote → Flow 3 (promotion check)
• /agency-ladder --export → Flow 4 (stakeholder artifact)

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Flow 1: Planning a New AI Feature

Step 1: Understand the Feature

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 STEP 1: UNDERSTAND THE FEATURE
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Before mapping agency, let's understand what the AI is doing.

  • What task is the AI performing?
  • Who is the user?
  • What's the consequence of AI being wrong?

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Questions to ask:

• "What's the AI's job in one sentence?"
• "What's the worst thing that happens if the AI is wrong?"
• "How would a user currently do this without AI?"

Step 2: Define V1 (Low Agency)

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 STEP 2: DEFINE V1 (LOW AGENCY)
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V1 is where you start: AI suggests, human decides.

  • What's the minimum capability that tests your hypothesis?
  • How will users override when the AI is wrong?
  • What data will you collect to inform V2?

V1 Pattern: High Control, Low Agency
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V1 questions:

• "What's the simplest version where AI helps but human decides?"
• "What override mechanism exists?"
• "What are you testing with V1?"
• "How long will you run V1 before considering V2?"

Step 3: Map V2 (Medium Agency)

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 STEP 3: MAP V2 (MEDIUM AGENCY)
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V2 happens when V1 proves reliability.

  • What categories could AI handle autonomously?
  • What still needs human judgment?
  • What's the escalation path?

V2 Pattern: Medium Control, Medium Agency
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V2 questions:

• "What did you learn from V1 that enables V2?"
• "What confidence threshold triggers auto-action?"
• "What scenarios still need human review?"

Step 4: Envision V3 (High Agency)

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 STEP 4: ENVISION V3 (HIGH AGENCY)
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V3 is the end state: AI acts autonomously.

  • What does full autonomy look like?
  • What's the human's role at V3?
  • Is V3 even desirable for this feature?

V3 Pattern: Low Control, High Agency
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V3 questions:

• "What would it take to trust the AI completely?"
• "What's the human doing at V3? (spot-check, escalation only?)"
• "Should this feature ever reach V3?"

Note: Some features should never reach V3. That's a valid outcome.

Step 5: Define Promotion Criteria

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 STEP 5: PROMOTION CRITERIA
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What must be true to move from V(n) to V(n+1)?

  □ Quality metrics stable for how long?
  □ What user correction rate is acceptable?
  □ What error patterns must be resolved?
  □ What monitoring must be in place?

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Promotion criteria categories:

• Quality: Accuracy, success rate, user satisfaction
• Safety: Error patterns documented, failure modes understood
• Operations: Monitoring in place, rollback plan ready
• Trust: User corrections decreasing, feedback positive

Output: Flywheel Table

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 AGENCY LADDER COMPLETE
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# [Feature Name] - Agency Ladder

## Progression Overview

| Version | Capability | Control | Agency | What You're Testing |
|---------|------------|---------|--------|---------------------|
| V1 | [describe] | High | Low | [hypothesis] |
| V2 | [describe] | Medium | Medium | [hypothesis] |
| V3 | [describe] | Low | High | [hypothesis] |

## Flywheel

| Version | What You're Testing | What You Learn | What Feeds Next Loop |
|---------|---------------------|----------------|----------------------|
| V1 | [hypothesis] | [insights] | [data for V2] |
| V2 | [hypothesis] | [insights] | [data for V3] |
| V3 | [hypothesis] | [insights] | [calibration] |

## Control Handoffs

**Override mechanism:** [how users correct]
**Escalation path:** [when AI defers]
**Feedback capture:** [how corrections improve system]

## Promotion Criteria (V1→V2)

- [ ] [Metric 1] stable for [duration]
- [ ] [Metric 2] above [threshold]
- [ ] [Condition 3] resolved
- [ ] [Monitoring] in place

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What next?

  1. Export to spec (/spec --ai)
  2. Set up post-launch calibration (/calibrate)
  3. Share with stakeholders
  4. Start building V1

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Flow 2: Reviewing an Existing AI Feature

Step 1: Map Current State

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 MAPPING CURRENT STATE
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Let's understand where this feature is today.

  • What's the AI currently doing?
  • How much human oversight exists?
  • What's working? What's failing?

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Questions to determine current level:

• "Does the AI act without human approval?" (If no → V1)
• "Does the AI handle some cases autonomously?" (If yes → V2)
• "Does the AI act autonomously most of the time?" (If yes → V3)

Step 2: Identify Gaps

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 GAPS ANALYSIS
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Based on the current state, here are potential gaps:

  □ Override mechanism exists?
  □ Quality metrics being tracked?
  □ Error patterns documented?
  □ Promotion criteria defined?
  □ Next level planned?

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Output: Current State Assessment

Generate assessment showing:

• Current agency level (V1/V2/V3)
• What's working
• What's missing for CC/CD compliance
• Recommended next steps

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Flow 3: Promotion Check

Promotion Criteria Checklist

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 PROMOTION CHECK: V[n] → V[n+1]
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Let's see if this feature is ready for more autonomy.

QUALITY METRICS
  □ Accuracy/quality metrics stable for 4+ weeks?
  □ No new error patterns emerging?
  □ User corrections decreasing over time?

SAFETY & TRUST
  □ Confident in all known failure modes?
  □ Override mechanism working well?
  □ User feedback positive?

OPERATIONAL READINESS
  □ Monitoring in place for new agency level?
  □ Rollback plan if quality degrades?
  □ Team aligned on promotion decision?

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Output: Promotion Assessment

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 PROMOTION ASSESSMENT
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Feature: [name]
Current Level: V[n]
Target Level: V[n+1]

VERDICT: [READY / NOT READY / NEEDS WORK]

✅ Passing:
- [criteria met]
- [criteria met]

❌ Blocking:
- [criteria not met]
- [criteria not met]

⚠️ Risks:
- [risk if promoted now]

RECOMMENDATION:
[Clear recommendation with reasoning]

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Flow 4: Export for Stakeholder Discussion

Generate a presentation-ready artifact:

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 AGENCY LADDER - [Feature Name]
 Stakeholder Discussion Document
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## Why This Matters

AI products need a fundamentally different approach than
traditional software. We can't "ship and forget."

This document outlines how [feature] will earn autonomy
over time, with clear gates at each level.

## The Ladder

[Flywheel table from Flow 1]

## Current State

[Where we are today]

## Next Steps

[What we're asking stakeholders to approve]

## Risks & Mitigations

[Key risks at each level and how we address them]

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Challenge Patterns

"Let's just ship V3" → "What happens when it fails? How do users recover? What failure modes are you willing to discover in production?"

"Users will trust it" → "How do you know? What's your evidence? Have you tested this with real users at lower agency?"

"We'll figure out V2 later" → "What must V1 prove to inform V2? If you don't know what you're testing, how will you know when to promote?"

"It's just a small feature" → "What's the consequence of AI being wrong? Even 'small' features can have significant trust implications."

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Integration with Other Commands

Before /agency-ladder:

• /spec --ai - Define what the AI is doing

After /agency-ladder:

• /calibrate - Post-launch calibration workflow
• /ai-health-check - Pre-launch validation

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Attribution

Framework: CC/CD (Continuous Calibration/Continuous Development) Source: Aishwarya Naresh Reganti & Kiriti Badam (Lenny's Newsletter) Adaptation: Agency ladder progression planning

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Remember

• Start with low agency, earn autonomy
• Each version tests a specific hypothesis
• Promotion requires proven reliability
• Some features should stay at V1 forever - that's valid
• Control handoffs must be designed, not assumed