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  • ›Claude Code
    Overview

    Setup

    • Sign In to Claude Code
    • Install Node.js
    • Install Claude Code

    Figma ↔ Code

    • Set Up Figma MCP
    • Turn Figma Frames into Code
    • Using Figma Links in Prompts
    • Figma to Code Best Practices
    • Bring Code Back to Figma

    Prototype

    • Start Your First Claude Code Session
    • Create Your Project Folder
    • Generate Your First Prototype
    • See Your Prototype Live
    • Get Back to Editing

    GitHub

    • Create a GitHub Account
    • Create a Repository on GitHub
    • Connect Your Local Project to GitHub
    • Save Your Changes Going Forward
    • Share Your Work (Let Claude Code Handle Git)

    Best Practices

    • How to Describe Your Design to Claude Code
    • Testing Your Prototype
    • Iterating Based on Feedback
    • Handing Off Work to Developers
    • Troubleshooting Common Issues
  • ›Cursor
    Overview

    Setup

    • Download and Install Cursor
    • Navigate the Interface
    • Edit Code with AI Assistance

    Prototype

    • Use Tab Completions
    • Chat with AI (Cmd+L)
    • Make Inline Edits (Cmd+K)
    • Build with Composer

    Design-to-code

    • Convert Designs to Components
    • Build Frontend with React and Tailwind

    Best Practices

    • Customize Your Workspace
    • Master Advanced Features
    • Best Practices and Team Workflows
  • ›GitHub Copilot
    Overview

    Setup

    • Install GitHub Copilot
    • Your First Code Suggestion
    • Workspace Setup for Designers

    Core Features

    • Autocomplete & Code Completions
    • Chat for Design Questions
    • Inline Code Explanations

    Prototyping Workflows

    • From Design to Interactive Prototype
    • Building Responsive & Interactive Layouts

    Developer Collaboration

    • Reviewing Developer Code
    • Communicating with Your Dev Team
  • ›GitHub
    Overview

    Setup

    • Create Your GitHub Account
    • Install Git on Your Computer
    • Understanding Git Basics

    Core Features

    • Clone Your First Repository
    • Making and Committing Changes
    • Working with Branches

    Developer Collaboration

    • Creating Pull Requests
    • Reviewing and Merging PRs

    Best Practices

    • Handling Merge Conflicts
    • GitHub Workflow for Design Teams
  • ›Build a Conversational UI
    Overview

    Foundations

    • What Is Conversational UI? (And What It Isn't)
    • Anatomy of a Chat Interface

    Building

    • Building Message Bubbles in React
    • Typing Indicators & Streaming Responses
    • Suggested Prompts & Conversation Starters

    Advanced Patterns

    • Managing Conversation Context
    • Error Handling & Fallback Design
    • Voice Interface Design Patterns

    Ship It

    • Accessibility in Conversational UI
    • Putting It All Together - Architecture Checklist
    • Agentic Conversational UI - When AI Takes Actions
  • ›Claude Design
    Overview

    Setup

    • What Claude Design Is (and Isn't)
    • Your First Prompt: The Four-Part Framework
    • Importing Assets: Screenshots, Docs, and Codebases

    Iteration

    • Iterating via Conversation
    • Inline Comments and Direct Edits
    • Tweaks: Explore Variations Without Chat

    Design-system

    • Extracting Your Design System
    • Publishing and Applying Your Design System
    • Team & Enterprise Setup

    Workflows

    • Prompt to Interactive Prototype
    • Prompt to Pitch Deck
    • Handoff to Claude Code for Implementation
  • ›Using AI UX Skills
    Overview

    Foundations

    • What Is Claude Code?
    • What a Claude Code Skill Is
    • Patterns Teach You, Skills Teach Your Agent

    Setup

    • Install a Skill Pack

    Core Features

    • How Triggering Works

    Best Practices

    • Working With Skills Day to Day
  • ›Claude Docs
    Overview

    Foundations

    • What a Doc Is, and Why It Is Not a Chat

    Working

    • The Anchored Comment: Brief It by Pointing
    • Watching It Work
    • Tabs, Sharing, and the Two Names Problem

    Judgement

    • When a Doc Is the Wrong Ask
  • ›Claude Slides
    Overview

    Foundations

    • The Question Slides Asks First

    Setup

    • The Empty Picker, and the Legacy Trap

    Working

    • Skipping the System, and What You Get
    • Editing by Comment, on a Canvas

    Handoff

    • The Export, Inspected

What’s new

  • Muse hits 5M and Claude Design sync
  • OpenAI Dots and Zombie UI
  • Shopify Agent Checkouts and Humane Design
  • All →

Topics

  • Adaptive & Intelligent Systems
  • Human-AI Collaboration
  • Trustworthy & Reliable AI
  • Natural Interaction
  • Performance & Efficiency
  • Privacy & Control
  • Accessibility & Inclusion
  • Safety & Harm Prevention
Previous: Vulnerable User ProtectionNext: Intent Preview
Human-AI CollaborationAgentic

Autonomy Spectrum

Provide a spectrum of autonomy levels - from passive suggestions to full autonomy - that users can adjust per task type, enabling granular control over how independently an AI agent operates.

What is Autonomy Spectrum?

The Autonomy Spectrum pattern replaces binary AI controls (on/off, assist/don't assist) with a graduated range of independence levels. Traditional AI interactions are either fully manual or fully automated, but agentic workflows demand nuance. A user might want their email agent to auto-sort messages without asking, but require explicit approval before sending any reply. This pattern provides four core levels - Observe & Suggest, Propose & Confirm, Act & Notify, and Full Autonomy - adjustable per task type. The key insight is that trust isn't global: users develop different comfort levels for different domains based on the agent's track record. By making autonomy granular and visible, this pattern prevents the all-or-nothing dynamic where a single bad experience causes users to abandon the agent entirely.

Problem

Traditional AI controls are binary - the AI is either on or off. But agents operate across a wide range of independence, and users need granular control over how much freedom the agent has per task type. Without this, a single bad experience at high autonomy causes users to abandon the agent entirely.

Solution

Provide a spectrum of autonomy levels (Observe & Suggest, Propose & Confirm, Act & Notify, Full Autonomy) that users can adjust per task or domain. Default to lower autonomy for new users and let trust build through demonstrated reliability before offering higher levels.

Real-World Autonomy Spectrum Examples

Implementation

Practice in Courses

Claude Code

Claude Code Course for Designers

23 lessons, free course

When to use Autonomy Spectrum, and when it backfires

Use it when

  • The tasks genuinely span a range of stakes and reversibility, so one setting can't fit all: auto-sorting mail unattended is fine, sending a reply in your name is not. A spectrum lets each task sit where its risk belongs.
  • Trust is earned unevenly and over time. Users get comfortable with an agent in one domain long before another, and the control has to track that per-domain record, not a single global toggle.
  • The user can see and change the level, and each level maps to visibly different agent behavior (asks first vs. acts then tells). A control that changes what the agent does, not just a label on a screen.

Don't, or minimize, when

  • Every task on the surface sits at one end: all low-stakes and reversible, or all high-stakes and irreversible. A spectrum there is fake choice. Ship the one correct default instead of four levels that collapse into it.
  • The levels don't actually change behavior. If 'Propose & Confirm' and 'Act & Notify' produce the same agent actions with different wording, you have a settings screen, not autonomy.
  • You can't reliably step autonomy back down. If the agent can be promoted on a good streak but nothing demotes it after a bad call, don't offer the higher rungs. One-way autonomy is a trap dressed as trust.

The trap

Autonomy creep: the level the user set and the freedom the agent actually takes quietly drift apart. Either the product defaults everyone to high autonomy because it demos well, or the agent promotes itself on a good streak and never steps back down after a mistake. The spectrum becomes decoration. It advertises graduated, revocable control while the agent operates a rung (or three) above what the user consented to, and the gap only surfaces when an irreversible action lands that the user assumed still needed their sign-off. Worse than a plain on/off switch, because at least a switch makes the user flip it themselves.

Take it into your own product

  1. 1

    Trust isn't global, so autonomy can't be either.

    Make the level per task or domain. A user who lets the agent auto-file receipts may never let it email a client. One dial for everything forces them to set it to the most dangerous task, which leaves the safe tasks manual and the whole feature feeling useless.

  2. 2

    The level has to change what the agent does, not what it's called.

    If moving from 'Propose & Confirm' to 'Act & Notify' doesn't change a single agent action, you built a labeled radio group, not autonomy. Each rung must map to a visibly different behavior: asks first, acts then tells, or acts silently.

  3. 3

    Autonomy must move down as easily as up.

    Trust is earned slowly and lost fast. If a good streak promotes the agent but a bad call doesn't demote it, you've built a ratchet, and ratchets are how autonomy creep happens. Let the user, and a failure, drop the level in one move.

  4. 4

    Default low, earn the rest.

    Start new users and new domains at the cautious end and let demonstrated reliability unlock the higher levels. Defaulting everyone to high autonomy because it demos well is the fastest way to burn trust the first time the agent is wrong.

  5. 5

    Show the current level where the action happens.

    The user should never have to guess whether the agent will ask first. Surface the active level at the point of action, not buried three screens deep in settings, so consent stays informed and current instead of set once and forgotten.

Save as a Claude skill

Save Autonomy Spectrum as a Claude skill

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More in Human-AI Collaboration

Contextual Assistance

Offer timely, proactive help and suggestions based on user context, history, and needs.

Human-in-the-Loop

Balance automation with human oversight for critical decisions, ensuring AI augments human judgment.

Augmented Creation

Empower users to create content with AI as a collaborative partner.

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Claude
Claude
Cursor
Cursor
GitHub
GitHub

Coding Agent Autonomy Controls

A permissions panel for a coding agent where each tool has its own autonomy level. Adjust the sliders and watch the live activity feed show exactly how the agent would behave at each setting.

Toggle to code view to see the implementation details.