agency-ladder

What this skill does

# 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.

---

## Entry Point

When this skill is invoked, start with:

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 AGENCY LADDER
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**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)

---

## Flow 1: Planning a New AI Feature

### Step 1: Understand the Feature

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 STEP 1: UNDERSTAND THE FEATURE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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?

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**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)

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 STEP 2: DEFINE V1 (LOW AGENCY)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**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)

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 STEP 3: MAP V2 (MEDIUM AGENCY)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**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)

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 STEP 4: ENVISION V3 (HIGH AGENCY)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**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

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 STEP 5: PROMOTION CRITERIA
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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?

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**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

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 AGENCY LADDER COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

# [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

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

What next?

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

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

---

## Flow 2: Reviewing an Existing AI Feature

### Step 1: Map Current State

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 MAPPING CURRENT STATE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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?

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**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

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 GAPS ANALYSIS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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?

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

### 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

---

## Flow 3: Promotion Ch