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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
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    • See Your Prototype Live
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    • Create a GitHub Account
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    • 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: Action Audit TrailNext: Trust Calibration
Human-AI CollaborationAgentic

Escalation Pathways

Design structured escalation triggers and handoff mechanisms so agents can pause and ask for human guidance when they encounter ambiguity, conflicts, or decisions beyond their authorization - without breaking workflow or losing context.

What is Escalation Pathways?

Agents will encounter situations they can't handle - ambiguous instructions, conflicting information, high-stakes decisions they're not authorized to make, or tasks that exceed their capabilities. The agent needs a structured way to escalate to the human without breaking the workflow, losing context, or creating anxiety. This is different from simple error recovery because the agent hasn't failed - it's recognized its own limitations. The pattern defines four escalation types: confidence-based (uncertainty threshold), permission-based (authorization limits), conflict-based (contradictory information), and capability-based (task exceeds abilities). Each escalation preserves full context, includes a recommended action with confidence level, and allows the agent to continue from where it paused after the user responds.

Problem

Agents encounter situations they can't handle - ambiguity, conflicts, authorization limits, or capability gaps. Poor escalation design either interrupts users too frequently (escalation fatigue) or too rarely (the agent guesses wrong on high-stakes decisions).

Solution

Design structured escalation triggers with context preservation, recommended actions with confidence levels, and multiple response options. Batch non-urgent escalations, learn from repeated answers, and let users set escalation sensitivity.

Real-World Escalation Pathways Examples

Implementation

Practice in Courses

Claude Code

Claude Code Course for Designers

23 lessons, free course

GitHub

GitHub Course for Designers

10 lessons, free course

Conversational UI

Build a Conversational UI

11 lessons, free course

When to use Escalation Pathways, and when it backfires

Use it when

  • The decision is above the agent's pay grade: it is high-stakes, irreversible, or outside the authorization you granted, and a wrong guess costs more than a pause.
  • The agent's own signals say it is out of depth: confidence is below threshold, the inputs conflict, or the task needs a capability it doesn't have.
  • A human can actually resolve it faster than the agent can flail. The handoff buys a real answer, not just a place to park the problem.

Don't, or minimize, when

  • The agent can recover on its own: retry, re-read, or pick the obvious default. Escalating a decision you could have made is just offloading work onto the user.
  • There is no human on the other end who can act, or they have no more context than the agent. A handoff into an empty room is worse than a guess.
  • The cost of being wrong is trivial and reversible. Interrupting someone to confirm a low-stakes, undoable action is how you train them to ignore the next escalation that matters.

The trap

The escalation to nowhere. The agent throws up its hands, drops the user into a ticket queue, a closed support form, or worse, loops them straight back to the same bot, and discards everything it knew. The human now starts from zero: re-explaining the problem the agent already understood, re-supplying context the agent already had. A handoff that loses state isn't an escalation, it's an eviction. It is worse than the agent guessing, because at least a guess keeps moving. Just as bad is escalating too late, after the agent has already sent the email, deleted the files, or charged the card, and the 'should I proceed?' arrives as a postmortem.

Take it into your own product

  1. 1

    Escalation is not failure. It's the agent knowing its own edges.

    Error recovery is for when the agent broke. Escalation is for when it didn't: the task is just above its authority, its confidence, or its capability. Frame the handoff as a competent colleague asking a question, not an error state apologizing. An agent that never escalates isn't confident, it's reckless.

  2. 2

    Pause before the irreversible step, not after it.

    An escalation that arrives after the email is sent or the card is charged isn't a decision point, it's a confession. The whole value is catching the high-stakes action while it can still be redirected. If 'should I proceed?' shows up as a postmortem, you escalated too late and the pattern bought you nothing.

  3. 3

    A handoff that loses context is an eviction, not an escalation.

    The single thing that makes escalation worth more than a guess is continuity. Carry the full state across: what the agent did, what it completed, the decision needed, the recommended action. If the human has to re-ask the user what the agent already knew, you've just added a worse middleman than no agent at all.

  4. 4

    Send the recommendation, not a blank question.

    'What should I do?' makes the user do the agent's thinking. 'I'd route this to Legal, 62% confident. Approve, or tell me otherwise' makes the decision a one-tap confirmation. The recommendation plus a confidence number is what turns an interruption into a quick yes.

  5. 5

    Tune the volume, and learn from the answers.

    Too many escalations and users rubber-stamp everything, including the one that mattered. Too few and the agent guesses wrong on the stakes. Let users set sensitivity, batch the non-urgent ones, and when the same answer comes back three times, offer to automate it. An escalation you never stop asking is a decision you failed to learn.

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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
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Notion
Notion

Agent Escalation Card

An escalation card that shows context, recommended action with confidence, and response options when the agent needs human input mid-task.

Toggle to code view to see the implementation details.