Before you ship an agent UI, decide whether an agent fits the user’s problem, define what it may do without review, and make sure people can understand and control its behavior. These decisions shape the product’s permissions, approval points, status messages, and recovery controls—not just its chat window.
1. Does the user’s problem actually call for an agent?
Start with the task a person needs to accomplish, not with the fact that your product can use AI. Microsoft Design defines an agent as an AI assistant designed to execute tasks, working with or for people. Some agent systems can identify and plan work, then take actions with limited direct supervision. That can be useful when delegation helps users reach their goal; it is unnecessary when a simpler interaction solves the problem.
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Write down the user need and the work the agent would take on. Then ask:
- Does the user want to delegate this task, or would they rather receive information and act themselves?
- Does the task involve a sequence of actions that an agent can meaningfully carry out?
- Would an ordinary interface, search, automation rule, or human-led workflow meet the need more clearly?
- What would count as a useful result from the user’s perspective?
Microsoft Design’s guidance is to begin with the customer problem because not every problem needs AI. An “agent” label is not a substitute for showing how delegation improves the experience. Microsoft Design’s UX guidance for agents sets out this problem-first approach.
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2. What can the agent do, and when does a person need to review it?
Set the agent’s boundaries before deciding how its controls should look. Specify which information it can access, which tools it can use, which actions it can take, and how much it can do without the user. The interface should make relevant capabilities and limits understandable when they matter, rather than leaving people to infer them from a successful or failed outcome.
Match approval to the action’s impact
Not every action needs the same level of review. For each action, consider its consequences and whether the user can readily reverse it. An action with meaningful or hard-to-reverse impact is a strong candidate for a preview and explicit confirmation before execution. Microsoft’s January 2026 agent-design guidance recommends human confirmation at critical, high-impact decisions and calls for surfacing uncertainty and limitations. Read Microsoft Design’s responsible agent-design principles.
A confirmation step is useful only if it gives the person enough information to make a choice. Show what the agent plans to do, what information or target the action concerns, and what will happen if the person approves. Avoid asking for approval after the consequential action has already occurred.
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Users need to know when an agent is active and what it is doing next. Replace a vague “working” state with status that accurately describes the current stage or next action. After an action, make the outcome visible so a person can tell whether it matched their intent.
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This matters for background and proactive agents as well as assistants that respond to a direct request. A person should have a way to inspect and control what the agent does, even when it is not waiting in a chat window. Microsoft Learn’s secure-agent guidance recommends communicating what an agent can and cannot do and exposing planned actions, approvals, and outcomes.
3. Can users understand, steer, correct, and stop it?
People need a workable mental model of the agent before and during use. Identify the system as AI, explain its scope and relevant limitations, and surface uncertainty where it could affect a decision. Where sources or data scope help someone verify an answer or action, make them available in context rather than hiding them behind an unclear status.
Control should cover more than approving a proposed action. Consider whether users can:
- Steer the result or clarify their intent.
- Correct a mistaken assumption or output.
- Dismiss an unwanted suggestion or response.
- Interrupt an action that is still underway, where interruption is possible.
- Change settings or turn off activation that is not wanted.
Make clear what a control changes and what it does not. For example, stopping further activity is different from undoing an action already completed. Microsoft Design recommends visible agent status and user control over settings and activation; Fluent 2’s responsible AI guidance also emphasizes clear AI presence, scope, limitations, meaningful status, verification, and controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review the whole interaction, not just the happy path
Walk through the experience from first use to changes over time. Check whether users can form reasonable expectations, follow ordinary activity, and recover when behavior is mistaken, ambiguous, or unwanted. Include situations where the system is uncertain, the user’s request is unclear, an action cannot be completed, or the agent’s behavior changes after an update.
Microsoft Research’s 2019 human-AI interaction work distilled earlier recommendations into 18 guidelines. The team began by collecting more than 150 AI-related design recommendations, then evaluated the guidelines through multiple rounds with UX and HCI experts. The guidelines are intended to support design decisions and discussion, not function as a simple checklist. They do not establish a measured effect for any one agent-interface pattern. Microsoft Research’s human-AI interaction guidelines describe the work and its lifecycle stages.
Compare design options against the same questions
If your team is choosing between interaction designs, use a shared set of comparison axes. These are a practical synthesis of the cited guidance, not a published scoring rubric.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Design question | What to examine |
|---|---|
| Autonomy and impact | What the agent can do without review, how consequential each action is, and whether it can be reversed. |
| Expectation-setting | Whether AI identity, capabilities, limitations, and uncertainty are clear at the moment they matter. |
| Legibility | Whether users can see current status, planned actions, relevant sources or data scope, and outcomes. |
| User control and recovery | How users can steer, approve, correct, dismiss, interrupt, or turn off the agent—and what happens after an action is complete. |
| Fit to task and context | Whether the interaction style, timing, and degree of proactivity suit the user’s goal and working context. |
Use the comparison to expose trade-offs and decide what to change before launch, not to create a pass/fail score. Microsoft Research describes its own 18 guidelines as aids to design decisions and discussion rather than a simple checklist. For a broader foundation, Microsoft Learn’s agent design foundations covers responsible AI, accessibility and inclusion, and human-centered design.
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