Design AI answers users can trust: use task-matched components and a density dial
A design checklist for AI answers: emit native components, match format to the task, and ship a user-controlled density dial.
UX Patterns
Ship AI edits users trust: five-part contract
A trustworthy AI editor is a host-app contract: scope the read, ask by default, name the always-ask actions, stop irreversible actions, and make undo native.
Persistent AI Feature Design: One Identity, One Mention, One Continuation Rule
A persistent AI teammate earns trust when it has one identity, one mention surface, one continuation rule, and visible permissions.
How to design AI agents users can supervise: presence, access, and routines
A persistent agent is easier to trust when its state, reach, and starting reasons are visible before the user has to ask.
Multi-Agent UI: One Conversation, Named Threads, Visible Work
A trustworthy multi-agent surface treats the top-level conversation as the manager, names each thread as a work unit, and makes memory, conflicts, effort, and usage visible.
Design an AI entry point that removes 'which tool?' without hiding what's happening
A merged chat, agent, and generation surface earns trust only when it names the active mode, explains the choice, shows progress, and lets users override it.
AI limit UI: preview cost, 5-hour resets, generate later
Treat AI output as a compute budget: preview cost, reset in 5 hours, and let heavy outputs wait with a ready alert.
Stop saying 'please wait': check your voice agent against the work-in-progress contract
A trustworthy voice agent acknowledges the request, narrates only meaningful progress, and closes with a completion state users can act on.
Design AI data features people trust: inherit permissions and metric definitions
A trustworthy data agent should answer only what the connected user can see and only in the team's defined metrics.
Voice input trust: raw or polished, settled transcript
A trustworthy voice input makes the transcription contract explicit: choose verbatim or smart, separate interim guesses from final text, and say when the transcript becomes authoritative.
Design full-duplex voice that survives interruptions: a checklist for turn-taking, tone, and backend handoff
A trustworthy full-duplex voice interface makes ownership explicit: the voice layer owns turn-taking, interruptions, and continuity, while the backend owns reasoning and tool calls.
Make AI Features Read-Only Panes That Cite Records and Stay in Workflow
When AI touches authoritative data, default to read-only access, cite the source record, and keep the feature inside the user’s existing workflow.
Screen-reader contracts for AI streaming, citations, errors
A checklist for accessible AI states: streaming, citations, voice, captions, alt text, and errors, with screen-reader announcements and recovery paths.
Redesign AI approval prompts so users stop missing threats
Reduce approvals, clarify risky actions, and reserve human judgment for the moments where a mistake is hard to undo.
Design AI memory settings users can audit, edit, and pause
Build AI memory settings users can audit: inventory stored memories, explain recall, scope where they apply, and offer edit, delete, or pause controls.
Ship a model picker with a safe default and legible alternatives
A model picker earns trust when the default is safe, alternatives are task-scoped, labels explain capability, and release notes set expectations.
Make AI answers feel fast and trustworthy before the first sentence lands
A streaming trust checklist for the first visible moment: fast first token, stable layout, named stage, stop control, and citations as they render.
Make AI Recommendations Trustworthy by Showing Reason, Alternatives, Confidence, Correction
Design AI recommendation UI that earns trust: show the reason, ranked alternatives, calibrated confidence, and a correction control.
Token Budgets: A UX Pattern for AI Coding Spend
Expose context costs, default to cheap deterministic steps, and make savings legible: a token-budget pattern for AI coding tools.
Design release-date answers that show confirmed, rumored, and unknown instead of guessing a date
A trustworthy assistant should answer a release-date question with a date-state, a source, and a next action, not a confident guess.
Design the AI Outage State: Name the Failure, Show What Still Works, and Give Users a Next Step
A clear degraded-service contract tells users what is broken, what still works, and what to do next.
Design AI confirmation flows that earn trust without making users click through danger
Stop stacking warnings by building risk-tiered confirmations that show what an agent will do, why, how to undo it, and when a human should take over.
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