Product & Strategy
MDX
Designing User Experiences for AI Agents
Make goals, progress, evidence, approvals, uncertainty, and control understandable to users.
Editorial review: clarity, operational relevance, safety boundaries, and source quality.
Start with a clear contract
Explain what the agent can do, what information it needs, and what will require approval. Users should not have to infer the system's authority from a chat box.
Before a task begins, make the contract visible: supported outcomes, needed data, accessible systems, expected duration, and actions requiring approval. Use structured inputs when precision matters, while keeping conversation for ambiguity. Do not imply broad competence through an empty chat box.
Show meaningful progress
Present milestones, current activity, sources, and pending decisions rather than streaming internal reasoning. Let users inspect or correct the working state when it affects the result.
Progress should expose durable milestones and evidence: what is complete, what the agent is doing, what it is waiting for, and what changed. Show sources and editable assumptions without streaming private chain-of-thought. For long tasks, give users a stable task page and notifications rather than requiring an open session.
Keep control close
Cancellation, editing, retrying, and escalation should be easy to find. Approval screens should describe the specific action and consequence, not merely ask whether to continue.
Controls should match the task state. Let users correct inputs, remove an item from scope, cancel queued actions, revise a proposal, retry a failed step, or escalate with context intact. Approval UI should show a diff and consequence, and any post-approval change should require renewed consent.
Practical example
An agent preparing a marketing campaign
The interface begins with audience, channels, budget, and prohibited claims. A timeline shows research, draft assets, compliance review, and scheduling. Every factual claim links to evidence. Before scheduling, the user sees exact recipients, dates, cost, and asset diffs. They can edit one message without restarting research; changed content returns only to compliance review.
Field checklist
Apply it in practice
- Explain capability, authority, data use, and expected duration.
- Show milestones, evidence, assumptions, and waits.
- Use specific diffs and consequences for approval.
- Preserve context through edit, cancel, retry, and escalation.
Decision framework
Questions to answer before you build
Good agent UX makes capability, authority, progress, evidence, uncertainty, and control visible without exposing noisy internal reasoning.
Is the contract clear before work begins?
Explain supported outcomes, needed information, accessible systems, expected duration, and which actions require approval.
What progress is meaningful?
Show durable milestones, completed artifacts, evidence, waits, and pending decisions rather than a stream of model activity.
Can users change course safely?
Support editing scope, correcting assumptions, cancelling queued work, revising proposals, retrying steps, and escalating with context intact.
Common failure signals
Watch for these warning signs
- Using an empty chat box to imply broader capability than the system has.
- Streaming chain-of-thought instead of useful evidence and state.
- Requesting approval without showing the exact action and consequence.
Selected primary references
Continue with the source material
These sources inform the wider editorial perspective for this topic. They are not presented as line-by-line citations for every statement.