AI Agent UI/UX Design

What an AI agent interface actually has to do

A traditional interface waits for the user. An agent acts while the user is somewhere else, which moves the interface's job from operating software to supervising work. Users arrive with different questions: what is it doing right now, what did it do while I was away, what is it about to do that I should stop, and how do I fix what it got wrong.

That is why agent products that bolt a chat window onto an automation engine struggle to earn trust. Chat is good at taking instructions and poor at showing ten runs in progress, three waiting for approval, and one that half-failed. The more autonomy an agent has, the more its interface should look like review tooling and the less it should look like a conversation.

Match the interface to the agent's autonomy

The right agent UI depends less on the model and more on how much the agent is allowed to do without asking. We design against four levels:

• Suggests, user acts: inline suggestions or a side panel. This is closer to a copilot, and the user stays the operator.
• Acts with approval: a plan preview before execution and an approval queue for anything above a risk threshold. Most B2B agents should start here.
• Acts alone, reports back: an inbox of completed runs with exceptions surfaced first and an audit trail for every run. Chat becomes one control among several.
• Runs continuously: monitoring views with volume, health, and failures per agent, plus a pause control that is always visible.

The core screens of an AI agent product

Most agent products need the same set of surfaces, whatever the domain. Skipping any one of them usually shows up later as a support ticket or a lost enterprise deal.

• Setup and instructions: where users define goals, connect tools, and set boundaries in plain language, with useful defaults instead of a blank prompt box.
• Plan preview: the proposed steps shown as an editable list before anything runs.
• Live run view: each step marked pending, running, done, or failed, labelled in plain language rather than tool-call names.
• Approval queue: pending actions ranked by risk, each with enough context to decide without opening five other tabs.
• Run history and audit trail: what happened, when, on whose authority, and what changed as a result.
• Output review: agent-produced work shown next to its sources and editable before it is applied.
• Failure and recovery: what failed, what had already completed, and the single action that resumes or rolls back.

AI agent UI patterns that build trust

Trust in an agent is not a visual style. It is the sum of small moments where the interface proves the agent is supervised. These are the patterns we reach for most often:

• Plan as contract: users edit the plan before execution, so what runs is what they agreed to.
• Plain-language activity logs: "Drafted a reply to the renewal email" instead of a raw function name.
• Reversible versus irreversible: approving a draft and approving a send get different visual weight and different friction.
• Honest confidence: low-confidence output looks different and invites a check, instead of every answer arriving with the same certainty.
• One-step correction: fixing a mistake takes one action, and the correction carries into the next run.
• A visible stop: pausing or cancelling is never more than one click away, even mid-run.

AI agent UI examples worth studying

Study agent products by the pattern each one demonstrates rather than by brand. The patterns transfer to your product; the visual styles mostly do not.

• Plan before action: coding agents such as Devin show the steps they intend to take and let users steer before and during the work.
• Background work, familiar review: GitHub Copilot's coding agent works asynchronously and hands back a pull request, reusing a review surface developers already trust.
• Diff and accept: Cursor's agent proposes changes as diffs that users accept or reject, so every edit stays inspectable.
• Permission before consequence: Claude Code asks before running commands or editing files, and ChatGPT agent asks for confirmation before consequential actions, so autonomy stays bounded by explicit permission.

How an agent UI engagement works with us

Agent work starts with the supervision model, not the screens: which actions need approval, what a failed run looks like, and what a user sees when they come back after a day away. Once those rules are agreed, the screens move quickly because every state has a reason to exist.

On the subscription ($4,917/mo) the first deliverable lands within 24 hours of starting, and a complete agent surface covering setup, plan, live run, approvals, history, and failure states typically takes two to four weeks of requests. Everything ships as build-ready Figma with named components and tokens that your engineers, or AI coding tools like Cursor and Claude Code, can implement directly. Focused one-off agent projects start from $3,000.