菜单
Design user experiences for autonomous AI agents that act on behalf of users - control panels, consent flows, action previews, audit trails, and undo mechanisms. Use when: agentic AI, autonomous agents, AI autonomy controls, agent UX, AI actions, computer use, agent consent, agent audit trail.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
WCAG 2.2 Level AA accessibility audit for AI product surfaces - color contrast, focus order, ARIA usage, keyboard navigation, motion/animation, reduced-motion respect, alt text, and form label association. Use when: accessibility audit, a11y review, WCAG audit, screen reader compliance, keyboard-only navigation, color contrast check, focus management, ARIA review, accessible AI UX.
Design conversational AI interfaces that feel natural, recover from failures gracefully, and build user trust through dialogue. Use when: chatbot UX, conversational UI, dialogue design, AI assistant interface, chat flow, turn-taking, AI persona voice, multi-turn context.
Design graceful failure experiences for AI products - hallucinations, uncertainty, wrong outputs, and edge cases. Use when: AI hallucination UX, error handling for AI, uncertainty design, graceful degradation, AI failure recovery, confidence thresholds, safe fallbacks.
Design feedback mechanisms that help AI systems learn from users - thumbs up/down, preference ranking, corrections, and human-in-the-loop escalation. Use when: RLHF UX, user feedback for AI, thumbs up down design, AI correction flow, human in the loop, feedback signal design, AI improvement loops.
Map human-AI interaction journeys - trust arcs, capability discovery paths, autonomy progression, and AI-specific touchpoints. Use when: AI user journey, human-AI interaction mapping, trust arc mapping, AI capability discovery, AI adoption journey, AI experience map.
Design how AI presents results across text, code, images, charts, and mixed media - response formatting, output hierarchy, and cross-modal transitions. Use when: AI output design, response formatting, AI results display, multimodal output, AI-generated content presentation, code output UX, AI visualization.
| name | AI Agent UX |
| description | Design user experiences for autonomous AI agents that act on behalf of users - control panels, consent flows, action previews, audit trails, and undo mechanisms. Use when: agentic AI, autonomous agents, AI autonomy controls, agent UX, AI actions, computer use, agent consent, agent audit trail. |
Design experiences for AI that doesn't just suggest - it acts. Booking flights, sending emails, executing code, making purchases. The AUTONOMY framework ensures users stay in control while AI operates at machine speed.
Agentic AI inverts the traditional UX model. In classical UX, users take actions and the system responds. In agentic UX, the system takes actions and users supervise. This requires an entirely new design vocabulary: previews instead of forms, audit trails instead of history, interrupts instead of navigation.
| Letter | Principle | Design Question |
|---|---|---|
| A | Action Preview | Can the user see exactly what the agent will do before it does it? |
| U | User Override | Can the user stop, modify, or redirect the agent at any point during execution? |
| T | Tiered Authority | Does the agent's freedom scale appropriately with risk level? |
| O | Observable State | Can the user see what the agent is doing right now, in real time? |
| N | Narrated Reasoning | Does the agent explain why it chose this action over alternatives? |
| O | Outcome Verification | Can the user verify that the action completed correctly? |
| M | Memory of Actions | Is there a complete, searchable log of everything the agent did? |
| Y | Yield to Humans | Does the agent know when to stop and ask for help? |
Not all actions require the same level of human oversight. Design a graduated control system:
| Level | Name | Agent Behavior | User Involvement | Example |
|---|---|---|---|---|
| 1 | Suggest | Recommends action, takes none | User must initiate | "I recommend replying with: [draft]. Send?" |
| 2 | Draft & Wait | Prepares the action, pauses for approval | User reviews and approves | Agent drafts email, shows preview, waits for "Send" |
| 3 | Act & Notify | Executes immediately, sends notification | User reviews after the fact | Agent files expense report, sends summary |
| 4 | Act Silently | Executes without notification | User can check audit log | Agent auto-categorizes emails |
| 5 | Full Autonomy | Executes complex multi-step tasks independently | User sets goals, agent reports outcomes | Agent manages a full sales outreach sequence |
| Risk Factor | Default Autonomy Level |
|---|---|
| Action is reversible + low cost | Level 3-4 (Act & Notify or Silent) |
| Action is irreversible OR high cost | Level 1-2 (Suggest or Draft & Wait) |
| Action involves external parties (sends emails, makes payments) | Level 2 maximum until trust is established |
| Action involves sensitive data | Level 1-2 with audit logging |
| User has explicitly granted higher autonomy | Respect the setting, but cap at the risk level |
Design rule: The Autonomy Dial should be a user-facing control, not a hidden system setting. Let users tune their own comfort level.
Before the agent acts, users need to understand what's about to happen.
Every consequential agent action should display a preview card with:
| Element | Content | Required? |
|---|---|---|
| Action summary | One sentence: what the agent will do | Always |
| Targets affected | Who or what is impacted (people, systems, data) | Always for external actions |
| Estimated impact | What changes as a result | For non-trivial actions |
| Alternatives considered | What else the agent could have done | When multiple valid options exist |
| Reversibility indicator | Can this be undone? How? | Always |
| Confidence level | How sure is the agent this is the right action? | When confidence varies |
While the agent is executing, users need visibility and control.
A real-time, scrollable feed showing:
| Component | Purpose | Update Frequency |
|---|---|---|
| Current step indicator | "Step 3 of 7: Sending confirmation email" | Per step |
| Progress visualization | Linear or branching progress bar | Continuous |
| Decision log | "Chose option A because [reason]" | Per decision point |
| Pause/stop controls | Always visible, always responsive | Permanent |
| Skip/redirect | "Skip this step" or "Do X instead" | Per step |
Critical rule: The stop button must work instantly. If the agent can't be stopped mid-action, the UI must make this clear BEFORE the action begins.
After every consequential action, provide an action receipt:
| Element | Content |
|---|---|
| What was done | Plain-language summary of completed actions |
| What changed | Before/after diff of affected data |
| Undo option | One-click reversal if action is reversible |
| Time-limited undo | For partially reversible actions: "You have 30 seconds to undo the email send" |
| Audit link | "View full activity log" |
| Action Type | Undo Pattern | Time Window |
|---|---|---|
| Data modification | Full revert to previous state | Unlimited |
| Message sent | Recall + replacement option | 30 seconds to 5 minutes |
| File deletion | Soft delete → permanent after 30 days | 30 days |
| Purchase/payment | Cancellation request + refund flow | Varies by provider |
| Multi-step sequence | Step-by-step rollback with checkpoint selection | Per step |
| Irreversible action | Prevention-only (no undo, must block before execution) | N/A |
New agents should not start at Level 5. Build trust incrementally.
| Phase | Duration | Autonomy Level | Unlock Condition |
|---|---|---|---|
| Onboarding | First 5 interactions | Level 1 (Suggest only) | User completes first review cycle |
| Building | Next 20 interactions | Level 2 (Draft & Wait) | >80% approval rate on previews |
| Established | Ongoing | Level 3 (Act & Notify) | User explicitly opts in |
| Trusted | After sustained use | Level 4 (Act Silently) | User configures specific action types |
| Delegated | Expert users only | Level 5 (Full Autonomy) | User sets goals + boundary conditions |
The cardinal rule: Users can always dial DOWN autonomy instantly. Dialing UP requires confirmation.
When multiple agents work together:
| Challenge | Design Solution |
|---|---|
| Which agent did what? | Color-coded agent identity in the activity feed |
| Agents disagree on approach | Surface the disagreement to the user with each agent's reasoning |
| Agent A's output feeds Agent B | Show the handoff explicitly: "Research Agent found 5 candidates → Outreach Agent will draft emails for your review" |
| Cascading failures | If Agent A fails, halt Agent B and notify user before damage propagates |
| Pattern | Why It Fails |
|---|---|
| "The AI will handle everything" messaging | Sets unrealistic expectations; users feel betrayed at first failure |
| Burying the stop button | Users feel trapped. Panic → permanent distrust |
| Asking permission for every micro-action | Decision fatigue. Users disable the agent entirely |
| No audit trail | "What did it do while I was asleep?" Users can't trust what they can't verify |
| Irreversible actions without explicit consent | One wrong autonomous action and the user never delegates again |
| Showing agent "thinking" animation without substance | A spinner is not transparency. Show what the agent is actually evaluating |
| Task | Framework Element | Key Deliverable |
|---|---|---|
| Design a new AI agent product | Full AUTONOMY framework | Autonomy Dial settings + Pre/During/Post action flows |
| Add agent capabilities to existing product | Trust Ramp Pattern | Phased rollout plan with unlock conditions |
| Audit agent UX for safety | Undo Architecture + Action Receipt | Gap analysis: which actions lack undo, preview, or audit? |
| Design multi-agent coordination | Multi-Agent Coordination table | Agent identity system + handoff visualization |
| Set autonomy defaults | Setting the Default Level table | Risk-matched autonomy levels per action type |
Works with: ai-trust-transparency (explaining agent decisions), ai-error-resilience (agent failure recovery), ai-safety-guardrails (agent action boundaries), ai-feedback-loops (rating agent performance).
