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agency-ladder

Plan V1–V3 AI autonomy progression with promotion criteria and control handoffs.

16|3|Updated Oct 23, 2025
Search Tags:#risk-assessment#product-strategy#stakeholder-communication#agency-ladder#ai-autonomy#promotion-criteria
One-click install
npx skills add https://github.com/breethomas/bette-think --skill agency-ladder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agency-ladder
Source: https://github.com/breethomas/bette-think/tree/main/plugins/bette-think/skills/agency-ladder
Command: npx skills add https://github.com/breethomas/bette-think --skill agency-ladder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Plan and communicate how an AI feature should earn autonomy over time to reduce risk, surface failure modes early, and create clear gates for promotion between versions.

Core Features & Use Cases

  • Versioned Progression: Break a feature into V1 (low agency), V2 (medium agency), and V3 (high agency) with clear capability descriptions and control handoffs.
  • Promotion Criteria & Safety Checks: Define measurable quality, safety, operations, and trust criteria required to advance between versions.
  • Stakeholder Artifacts: Export presentation-ready ladders and flywheel tables for alignment, workshops, and launch decisions.
  • Use Case: Run this skill when planning a customer support bot, code review assistant, or recommendation engine to ensure you start with human oversight and only increase autonomy when data supports promotion.

Quick Start

Create an agency ladder for a new AI customer support feature mapping V1–V3 capabilities, promotion criteria, override mechanisms, and monitoring requirements.

Frequently Asked Questions about agency-ladder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I plan AI autonomy progression for a new product feature?▼

Plan AI autonomy progression by mapping V1 to V3 capability ladders with defined control handoffs. This creates versioned gates that reduce risk, surface failure modes early, and ensure features earn autonomy through measurable promotion criteria.

What promotion criteria should I use to advance AI feature versions?▼

Promotion criteria for AI feature versions should include measurable quality, safety, operations, and trust metrics. Defining these specific gates ensures features only advance from low to high agency when supporting data validates readiness for the next version.

How do I design control handoffs and rollback mechanisms for autonomous AI agents?▼

Design control handoffs and rollback mechanisms by documenting override requirements within an AI autonomy roadmap. This establishes clear protocols for human oversight and defines monitoring triggers that revert agency back to safer versions when failure modes appear.

Can I use this approach for stakeholder communication during feature launch reviews?▼

Yes, you can generate presentation-ready ladders and flywheel tables for stakeholder communication during feature launch reviews. These artifacts align product and engineering teams on autonomy gates, promotion checks, and monitoring requirements for AI features.

What is the best way to structure an AI autonomy roadmap for a customer support bot?▼

Structure an AI autonomy roadmap by breaking the customer support bot into V1 low agency, V2 medium agency, and V3 high agency capabilities. Map clear promotion criteria, override mechanisms, and monitoring requirements to safely increase autonomy over time.

When should I not use a versioned AI autonomy ladder for product planning?▼

Avoid a versioned AI autonomy ladder when a feature lacks measurable safety, trust, or operational quality metrics for promotion criteria. Without clear data gates to validate readiness between versions, progressive autonomy cannot be safely granted or monitored.

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