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Desktop AI agents use an observe–decide–act loop: they receive information about an app’s current state, interpret a user’s request, propose an action, and rely on software in the app’s environment to carry it out. The agent then needs a new observation to tell whether the screen changed as expected. Depending on the integration, that information may be a screenshot, structured interface data, or another programmatic representation; actions may be mouse and keyboard inputs or executable code. This is why an agent’s ability to “see” a screen is only part of the story: it must also identify the right control, operate it in a live session, and do so safely.
How do desktop AI agents see and use a computer?
A desktop agent does not perceive an app as a person does. It receives a representation of the interface from an application-managed environment and uses that representation, together with the instruction and prior interaction context, to decide what to do next. In a visual workflow, the representation is often a screenshot. Other integrations may supply structured information or let the agent act through code.
A useful way to understand the system is to separate it into three parts: observation, agent decision, and execution. The model interprets what it has been given and proposes an action. The application’s integration code translates or executes that action in the target environment, captures the result, and supplies a fresh observation. The model then decides whether it has reached the requested state or should continue.
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- Decision: The model relates the instruction to that state and chooses an action, such as clicking a control or entering text.
- Execution and feedback: The application-managed runtime performs the action and returns a new observation or output.
This is a loop, not a single command. A click might open a menu, trigger a dialog, fail to register, or change the layout. Without another observation, the agent cannot reliably know which happened. Microsoft Foundry’s documentation describes a screenshot–action–screenshot loop, while a 2025 review by Pascal J. Sager and coauthors organizes computer-use agents around their environment, observation and action spaces, and agent behavior.
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What happens between an instruction and a click?
1. The system identifies relevant interface information
In a screenshot-based approach, the agent receives an image of the screen. It must interpret the layout and determine which visible elements matter to the task. A screenshot can show visual context such as labels, dialogs, and menus, but the image itself does not guarantee that the model has identified the correct control or understood its function.
Some integrations use a different route. OpenAI’s documentation describes code execution, in which a model writes code using tools such as Playwright or PyAutoGUI, as well as a computer tool that returns structured mouse and keyboard actions for application code to translate. Microsoft Foundry distinguishes desktop computer use from browser automation: its guidance points to computer use when an interaction extends beyond the browser or DOM parsing is unavailable, and browser automation for web-only interactions.
2. The agent grounds the request in the interface
To act on an instruction such as “click Submit,” the agent must connect the words to a particular control or location. Microsoft Research describes this as GUI grounding: translating an instruction into the exact screen coordinates for the target. The agent may understand the general purpose of the request yet still choose the wrong button, misread a label, or target a location based on an outdated screen.
3. The runtime carries out the proposed action
A model’s proposed action is not the same thing as an action already performed. In the documented integrations, application code or an execution service is responsible for running the relevant script or translating structured input into mouse and keyboard behavior. OpenAI’s integration guide says the application runs the script in its isolated browser or desktop environment and returns output, including screenshots. Microsoft Foundry likewise describes UI actions such as clicking, typing, and scrolling within its computer-use flow.
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4. A new observation determines what to do next
After execution, the agent needs current information about the app. If the page navigated, a dialog appeared, or an action did not work, that changed state may alter the next step. An integration also has to manage the live environment: OpenAI’s guide distinguishes the API conversation from the execution environment, so an application may need to preserve the relevant session state between calls. Anthropic’s documentation similarly describes an application-managed loop that sends actions to a desktop environment and returns screenshots or command output.
Why can an agent misread or miss a control?
Visual recognition and reliable operation are separate challenges. The system must interpret the screen, resolve which element matches the request, and select a location or action that the runtime can carry out. A control may be small, visually similar to another control, obscured, or positioned differently after the interface changes. A screenshot can also be resized to fit model limits, which creates a coordinate-mapping requirement.
Microsoft Research reports 65% success for grounding models in the context of its article. It separately reports Phi-Ground results of 55% accuracy on the ScreenSpot-Pro benchmark and 36.2% on UI-Vision. These are benchmark results, not a general estimate of the chance that a deployed desktop agent will complete ordinary work. They measure grounding in the named evaluation settings, not every step in a full task or every app.
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The same Microsoft Research article says Phi-Ground’s performance depends on how instructions and images are presented and on image-token detail. In the experiments it describes, benefits plateaued at approximately 2,000 image tokens. That finding belongs to those experiments; it is not a universal image setting for every model or integration.
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For image resizing, Anthropic’s documentation highlights a practical implementation detail: coordinates returned for a resized image must be mapped back to the original screen space. The integration also needs to respect the model’s image limits. If the mapping is wrong, an otherwise plausible coordinate can land on a different control in the actual desktop.
The size of the field is another easy fact to misinterpret. Sager and coauthors’ 2025 review examined and classified 86 computer-use agents and 33 related datasets. Those counts describe the review’s coverage; they do not mean there are currently 86 commercial products or that all of them remain available.
Which interaction approach fits an app?
There is no evidence here for a universal winner among screenshots, structured browser information, APIs, and executable code. The right choice depends on what the application exposes, which parts of the environment the task must reach, and who is responsible for executing and securing actions.
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|---|---|---|
| Screen-based computer use | The agent needs to work through a visible interface, including desktop software or browser interactions not available through a direct API. | Can the agent identify the right control? How are screenshots captured and resized? How are coordinates mapped to the original screen? |
| Browser automation or structured browser information | The work is limited to web pages and the integration can use browser automation or page structure such as the DOM. | Is the interaction web-only? Is the needed page structure accessible to the automation? |
| Application API or another programmatic interface | The application offers a direct interface that can perform the required operation. | Does the interface cover the task, and how will the integration authenticate, validate, and handle results? |
| Code execution | The model and integration can execute scripts in an application-managed environment, using tools such as Playwright or PyAutoGUI where appropriate. | Who runs the code, preserves session state, validates actions, and returns the output? |
The table describes distinctions in the documented approaches, not a performance ranking. The reviewed sources do not establish that APIs, DOM information, screenshots, or accessibility trees are always faster, more accurate, or more robust than one another. A structured interface or API is an alternative to screen interaction where available, but suitability depends on the specific app and task.
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Microsoft Copilot Studio describes computer use as a way for an agent to use a virtual mouse and keyboard to select controls in websites and Windows applications, including where no direct API is available. Microsoft Learn’s Copilot Studio documentation puts the point this way: “By using computer use, agents can complete tasks even when there’s no API to connect directly to the system.” This is a description of the product’s intended capability, not proof that every application or workflow will work reliably.
What should an implementation preserve?
When evaluating or building a computer-use flow, treat the model and the execution environment as separate components. The model interprets observations and proposes actions; application code supplies the environment, carries out actions, and returns results. That division determines where session continuity, action validation, and error handling belong.
- Scope: Establish whether the task is browser-only or must reach the wider desktop. Microsoft Foundry recommends browser automation for web-only interactions and computer use when work extends beyond the browser or DOM parsing is unavailable.
- Available structure: Check whether a suitable API, DOM, or other programmatic interface exists before relying on visual coordinates. The available sources do not support a general accuracy or speed ranking among these choices.
- Runtime ownership: Identify which application component runs scripts or translates structured actions, captures output, and keeps the relevant live session available.
- Image handling: Record image-size limits, resizing behavior, and how coordinates in a resized screenshot map back to the original screen.
- Action feedback: Return an updated observation after actions that may change the interface, so the agent can respond to the resulting state rather than assume success.
- Validation: Distinguish simulated examples from production code. Microsoft Foundry’s sample guidance makes that distinction and calls for production code that validates and executes actions.
What are the main security and privacy risks?
A desktop agent may be able to read information on screen and interact with applications under the permissions granted to its environment. This makes its access and allowed destinations part of the design, not a detail to add after the workflow works. Microsoft Learn’s Foundry documentation warns: “The computer use tool comes with significant security and privacy risks, including prompt injection attacks.”
Prompt injection is a risk because content encountered during a task may attempt to influence the agent’s behavior. Screen access and desktop control can also expose sensitive information or enable unintended changes. Microsoft recommends testing in a sandbox or virtual machine without sensitive data. For ongoing use, Microsoft Copilot Studio’s guidance recommends a computer-use account with only the permissions it needs and restricting web access to trusted sites.
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Be precise about what an allow list does. Microsoft’s Copilot Studio guidance notes that an allow list can restrict interaction without necessarily preventing navigation to an unlisted site. Do not treat a destination restriction as a complete guarantee that the agent cannot reach other sites; verify what the particular control blocks in the deployed configuration.
A prudent implementation should review the full chain: the model’s input, the data visible in the environment, the permissions of the account, the destinations the runtime can reach, and the code that validates and executes proposed actions. Sandbox testing reduces exposure during development, but does not remove the need to examine production access and controls.
How should accessibility shape computer-use design?
Accessibility is both a potential benefit and an implementation responsibility. Microsoft’s agent design guidance says AI can remove barriers for people with disabilities but can also create new ones, and calls for accessibility practices in both the visual agent layer and its communication. For a desktop agent, the experience includes more than whether it can locate a button: users also need understandable communication about what the agent is doing and what happened.
The available sources do not establish a technical comparison between accessibility-tree-based and screenshot-based agents. It would therefore be too broad to claim that one approach is inherently more accessible. Evaluate the actual app, task, and user needs, and consider how the agent communicates progress, errors, and outcomes as well as how it identifies controls.
What to expect from a desktop agent in practice
Think of a computer-use agent as a system that interprets a changing interface and operates it through an integration—not as a person looking at a screen, or as a single click that guarantees completion. Its reliability depends on the quality and freshness of its observations, how well it grounds instructions in the interface, the runtime’s execution and session management, and the permissions and validation around the task.
For a specific workflow, define the target application and scope first, then check what structured interfaces are available, how actions will be executed, and how the system will confirm the resulting state. If screen-based interaction is needed, account for image limits and coordinate mapping. Finally, test in an isolated environment and set permissions and destination controls deliberately. Those decisions make the difference between a plausible demo and an integration whose behavior can be understood and managed.
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