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Before shipping an AI agent interface, decide whether an agent is actually needed, define what it may do without approval, and make sure users can understand and control its behavior. These decisions shape the UI: what it discloses, what it asks users to confirm, and how people recover when the agent gets something wrong.
1. Does the user problem need an agent?
Start with the task a person needs to complete, not with the fact that your product can include AI. Microsoft Design defines an agent as an AI assistant designed to execute tasks, working with or for people. An agent may use instructions, knowledge, actions, skills, and memory; some systems can identify a goal, plan, and act with limited direct supervision. But not every customer problem needs AI, as Microsoft Design cautions in its UX design for agents guidance.
Ask what delegation would improve for the user. Is the task repetitive, multi-step, or difficult to complete with the existing interface? Does the user want the system to take action, or simply need clearer information or a more direct control? If a conventional workflow solves the problem more predictably, adding an agent may create uncertainty and oversight work without addressing a real need.
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- Describe the user’s goal in plain language, without naming a model or AI capability.
- Identify which steps the user wants help with and which decisions they want to retain.
- Consider the cost of a wrong action, the effort required to review the result, and whether a simpler interaction would be easier to understand.
The answer should determine whether an agent belongs in the product and, if it does, what job it is there to do. Avoid designing a broad “do anything” assistant when the need is a specific, bounded task.
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2. What can the agent do, and when must a person review it?
Set the agent’s boundaries before choosing its interaction pattern. Specify which information it can access, which tools it can use, what actions it can take, and which actions require a person’s approval. The UI should explain relevant capabilities and limitations where a user makes a decision—not bury them in a general description.
Autonomy is a product decision with interface consequences. If an agent can act in the background or proactively, people still need a way to see that it is active, inspect what it has done, and change or stop its behavior. Microsoft’s secure autonomous agent guidance recommends making planned actions, approvals, and outcomes visible. Its January 27, 2026 agent-design guidance recommends human confirmation at critical, high-impact decisions.
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Match approval to impact
Do not ask for confirmation on every inconsequential step if that makes the workflow needlessly cumbersome. Instead, identify actions that could materially affect a person, account, data, or workflow, and require review before those actions occur. Consider reversibility as well as impact: an easily undone formatting change is different from an irreversible or consequential action.
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Make activity and outcomes legible
Use accurate status and next-step messages rather than a vague “agent is working” state when the next action matters. Tell users when the agent is active, what it is doing, and whether it is waiting for input or approval. After an action, make the outcome visible so a person can check whether it matched their intent. Microsoft’s security guidance for autonomous agents emphasizes communicating what an agent can and cannot do and exposing plans, approvals, and results.
3. Can users understand, steer, correct, and stop it?
Users need a practical mental model of the agent before and during use. Identify the system as AI, explain its scope and limits, and surface uncertainty when it affects a decision. Where sources or data help users verify an answer, make them accessible. Show what the system is doing and provide controls that let people steer the result, correct mistakes, dismiss unwanted behavior, or interrupt the agent.
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Microsoft Design recommends visible agent status and user control over settings and activation in its agent UX guidance. The Fluent 2 responsible AI guidance likewise calls for clear AI presence, scope, limitations, data-use explanations, meaningful status, output verification, and user controls. These are interface decisions, not simply statements of principle: a user must be able to find and use the relevant control at the moment it matters.
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Review the experience across more than the successful first task. Microsoft Research’s 18 human-AI interaction guidelines organize considerations across first use, ordinary use, errors, and changes over time. Walk through each stage with realistic user goals and failure cases:
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- First use: Can users tell they are interacting with AI, what it can do, and what they remain responsible for?
- Ordinary use: Can they see the agent’s status, understand what happens next, and steer the outcome?
- Mistakes or unwanted behavior: Can they correct an error, undo or dismiss an action where possible, and interrupt the agent?
- Change over time: If the agent’s behavior, capabilities, or data access changes, will users be able to understand the difference?
These guidelines are design aids for discussion, not a pass-or-fail checklist. Microsoft Research reports that its team began by collecting more than 150 AI-related design recommendations in 2019, then synthesized and evaluated its work with UX and HCI experts to produce 18 guidelines. The source does not establish a quantified effect for any one interface pattern, so use the guidance to frame design choices rather than claim that a particular control guarantees trust or success.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare design options before choosing one
If your team has multiple viable concepts, assess them against the same questions. This comparison is a practical synthesis of the cited guidance, not a published scoring rubric.
| Decision area | What to examine |
|---|---|
| Autonomy and impact | What can the agent do without review? How consequential and reversible are those actions? |
| Expectation-setting | Are AI identity, capabilities, limitations, and uncertainty clear when they matter? |
| Legibility | Can users see the agent’s status, planned actions, relevant data or sources, and outcomes? |
| User control and recovery | Can people steer, approve, correct, dismiss, interrupt, or turn off the agent in practical ways? |
| Fit to task and context | Do the interaction style, timing, and degree of proactivity suit the user’s goal and working context? |
Use the comparison to expose trade-offs and unresolved questions. It should help the team decide which design best fits the user’s task and what still needs clarification—not turn broad guidance into a claim that an option is proven effective.
Quick Recap
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