Designing for AI agents: UX patterns for agentic products
Agentic products are software that takes actions on a user’s behalf, so their UX must help people delegate, supervise and stay in control. Core patterns include clear goal setting, showing the agent’s plan before it acts, visible progress, approvals for consequential steps, easy undo, plain explanations of what was done and why, honest handling of uncertainty and errors, and controls over what the agent may access. Good agentic UX builds trust by making the agent’s work visible and reversible.
What is different about designing for agents?
Most interfaces wait for the user to click. AI agents take initiative: they plan steps, use tools and change things in the world. That shifts the user’s role from operator to supervisor, and the interface’s role from controls to coordination. People need to say what they want, see what the agent intends, step in when needed and understand what happened. Our UX principles for AI design cover the foundations.
What are the core agentic UX patterns?
- Goal setting: Help users describe the outcome, with examples and constraints, not just a blank prompt.
- Plan preview: Show the steps the agent intends to take before it starts.
- Progress visibility: Show which step is running, what has been done and what is next.
- Approval gates: Pause for confirmation before sending, paying, deleting or publishing.
- Undo and rollback: Make actions reversible where possible, with a clear window.
- Explanations: Say what was done, why, and which sources or data were used.
- Uncertainty signals: Flag low confidence and ask rather than guess.
- Permission controls: Let users choose which apps, data and actions the agent can use.
How should approvals be designed?
Approval design is covered in depth in human-in-the-loop AI. The goal is fewer, more meaningful approvals, not a pop-up for every step.
How much autonomy should the agent have?
Let users choose the level per task, and start conservatively. Trust grows as people see the agent get things right.
How should agents handle errors?
- Say clearly what failed and what the agent tried
- Offer next steps: retry, change the request or hand to a person
- Never claim success when a step failed
- Keep partial work so users do not lose progress
- Log errors so teams can improve the agent
How do you build trust over time?
- Start small: Begin with low-risk, high-value tasks.
- Be transparent: Show plans, sources and results every time.
- Be consistent: Use the same patterns for approvals, progress and reports.
- Give control: Make it easy to pause, adjust permissions or turn the agent off.
- Learn from feedback: Let users rate results and correct the agent.
What does this look like in a real product?
In an accounting app, a user asks the agent to chase overdue invoices. The agent replies with a plan: find invoices more than 30 days late, draft polite reminders, skip any marked as disputed, and send after approval. It lists 12 invoices and shows the drafts. The user edits one message, removes a client she prefers to call, and approves. The agent sends the rest, shows a summary with links to each email, and offers undo for ten minutes. The next week, she lets it send reminders automatically for invoices under a set amount, with a weekly report.
What mistakes should you avoid?
- Hiding what the agent is doing behind a spinner
- Asking for approval so often that people stop reading
- Letting the agent claim success when a step failed
- Giving no way to pause, undo or change permissions
- Designing only the happy path
How do you measure agentic UX?
- Task completion rate without manual rework
- How often users override or undo agent actions
- Time saved compared with doing the task manually
- Trust and satisfaction scores over time
- Errors caught at approval versus after the fact
The best agentic products feel like working with a capable colleague: clear about what they will do, honest about what went wrong and easy to direct.
Design for supervision first, autonomy second. As users see the agent behave predictably, they will hand it more work on their own terms, and that earned trust is what makes agentic products succeed.
Test agent flows with real users early, just as you would any new interface.
Then refine the patterns based on what you observe in those sessions.
How UIUX Studio helps
We design agentic products and AI features, from delegation flows and approvals to activity logs and trust patterns. Talk to our product design and Agentic AI & Automation teams.
Frequently asked questions
What is agentic UX?
Designing products where AI agents act for users, focusing on delegation, supervision and control.
How is it different from chatbot design?
Agents take actions, so plans, approvals, progress and undo matter as much as conversation.
Should every action need approval?
No. Gate consequential actions and let low-risk ones run with reporting.
How do you show what an agent did?
With an activity log, summaries, sources and undo options.
How do you build trust?
Start small, be transparent and give users control.