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AI agents interact with apps only through tools and access that a host or application makes available. A model can suggest an action, but a connected identity, provider permissions, and host policy determine whether that action can run—and what it can reach.
How do AI agents interact with apps?
The interaction is a controlled sequence, not a model reaching directly into an app. The model chooses or proposes an operation; the host or client checks whether it is allowed; a runtime sends the API request or performs the interface action; the app returns a result; and the agent uses that result to decide what to do next.
- The host exposes a set of actions. These might be defined API operations, tools provided by an MCP server, or computer-use actions such as clicking and typing.
- The model proposes an action. For a tool, it can return a structured request. For computer use, it can return a suggested interface action based on a screenshot and prompt.
- The host and provider check access. The host can allow, ask for approval, or deny an action. Separately, the identity and permissions used to connect to the provider determine which account resources are accessible.
- A runtime executes an allowed action. It might send a request to an API or carry out a click or keystroke in an application environment.
- The app returns a result. The agent can then interpret the response or updated screen and propose another action.
These checks are distinct: a model’s ability to describe an action does not authorize it, and an approval prompt does not grant an identity access that the provider has not allowed.
What does app access and permission actually cover?
There are usually at least two permission layers. Provider authorization determines what the connected identity can access. Host policy determines which actions the agent is allowed to attempt and whether a person must approve them. Workspace or administrator settings may add further restrictions.
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Identity determines whose access is used
An API or MCP server call runs with some identity: for example, a user’s identity or a separate workload or agent identity. Google Cloud’s MCP documentation says that actions made through MCP using a user’s identity are attributed to that user and inherit that user’s resource permissions. In other words, connecting a tool does not inherently give an agent access to every account or resource; the identity and its permissions matter.
For production systems, Google recommends a separate agent or workload identity with the minimum permissions needed, together with logging. It also describes using IAM attributes to restrict read or write tool use on important resources. For OAuth connections, access is bounded by the scopes the user authorizes; the AI application does not receive the user’s raw credentials.
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Approval controls are not the same as provider authorization
An approval prompt answers whether a particular action may proceed in a host or session. It does not expand the connected identity’s provider permissions. The reverse is also true: having provider access does not necessarily mean a host will expose or allow every action.
For example, ChatGPT’s app-permission documentation separates provider authorization, action controls, workspace app settings, role controls, and app permissions. The available controls vary by account, app, connected account, and workspace. Changing an app permission does not disconnect the account or revoke permissions already granted by the provider; to stop future access, disconnect the account or unlink it with the provider.
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Other systems implement their own policies. Anthropic’s Managed Agents permission policies describe allow, ask, and deny outcomes for server-executed agent and MCP tools, with evaluated permissions recorded in event records. In its documented auto path, a server-denied call cannot be overridden by user confirmation. OpenAI’s Agents SDK documents configurable approval requirements and callbacks for hosted MCP tools. These are product-specific designs, not a universal permission standard.
How is an API or MCP tool call different from computer use?
An API or MCP tool call targets a defined operation. Computer use operates through the application’s visible interface. The difference affects what the agent can do, how it is authorized, how failures occur, and which runtime executes the action.
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| Dimension | API or MCP tool | Computer use |
|---|---|---|
| Action surface | Defined operations or tools, often with structured inputs and outputs. | Visual actions such as clicking, scrolling, typing, or navigating through an interface. |
| Identity and scope | The server call uses an identity and permissions, such as an authorized user’s OAuth scopes or a service identity’s access. | The action occurs in a target environment where the application is already available; what that environment can access depends on its account and setup. |
| Permission controls | Can include host allowlists, per-tool approval settings, provider permissions, and workspace restrictions. | Can include host policy and user confirmation for actions; the environment should also be controlled and isolated appropriately. |
| Execution | A client or host invokes the tool or backend operation after policy checks. | A client-side handler or application-controlled runtime performs the UI action and returns the resulting screen state. |
| Typical failure mode | A request may be rejected, lack scope, or return an application or API error. | A click or keystroke may affect the wrong control, miss a screen change, or produce an unexpected interface state. |
MCP is a protocol route between an MCP client and an MCP server; it does not, by itself, grant an agent an entire user account or make every server tool available. The server authenticates the client, and the identity or token used determines resource access. OpenAI’s MCP authentication guidance describes protected-resource and authorization-server metadata, a resource parameter, supported scopes, and an authorization-code flow using PKCE with the S256 challenge. It also advises implementers to plan for token revocation, refresh, and scope changes. Those are implementation details, not a guarantee that every MCP-capable product supports an identical flow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What happens during computer use?
In Google’s Gemini API Computer Use flow, the client sends the model a prompt and screenshot. The model returns a suggested function call representing a UI action; the client-side code then executes an allowed or user-confirmed action in the target environment, captures the updated state, and continues the loop.
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“The model analyzes the screen and the prompt, returning a response which includes a suggested
function_callrepresenting a UI action (such as a click, scroll, or keystroke).”
The wording matters: the model suggests the action; the client or runtime decides whether and how to execute it. Anthropic describes its computer-use tool in a similar client-toolset model: the application runs each call in an environment it controls and implements the loop that sends actions to the environment and returns results. For tasks confined to webpages, Anthropic says its browser-use tool is a closer fit than whole-desktop computer use.
Computer use is less constrained by a fixed list of API operations, but it is also sensitive to screen state and can make consequential mistakes. Google recommends using a sandboxed virtual machine or container and a client-side action handler. While its Computer Use feature is in preview, Google advises close supervision for important tasks and avoiding critical decisions, sensitive data, or actions where serious errors cannot be corrected.
How should you choose between an integration and computer use?
- Prefer a defined API or MCP tool when the app offers the exact operation needed and you want structured inputs, bounded actions, and explicit per-tool controls.
- Consider computer use when the task genuinely depends on interacting with a visible interface and there is no suitable structured operation exposed.
- Match the identity to the task. Use only the access the workflow needs; production automation should not casually reuse a person’s broad account permissions.
- Set host policy deliberately. Decide which actions are allowed, which require approval, and which should be denied. Treat provider authorization and workspace restrictions as separate controls.
- Assess the consequence of an error. Consider reversibility, sensitive data, and whether a person can inspect or correct the result before an irreversible action occurs.
- Check the current product documentation. Tool names, supported models, availability, approval behavior, and account or plan eligibility change across products and over time.
How common are MCP tools and browser actions?
The MIT AI Agent Index (2025), published in the FAccT ’26 proceedings in June 2026, reported MCP support for 20 of the 30 indexed agents it documented. It also reported that all 5 of the indexed browser agents manipulated web pages through click, type, or navigate actions. These are counts within the Index’s documented sample—not market-share estimates or a census of deployed agents.
Which documentation explains the controls?
- ChatGPT: OpenAI Help Center documentation on app permissions, covering provider authorization, action controls, workspace settings, and the effect of disconnecting an account. The page was accessed October 3, 2026.
- Gemini API: Google AI for Developers documentation on Computer Use, including the screenshot/action loop and safety guidance. The page was accessed October 3, 2026.
- Google Cloud: Google Cloud documentation on MCP identity and access, including user, workload, and agent identities. Its MCP page identifies an update on September 30, 2026.
- Anthropic: Documentation for computer use, browser use, and Managed Agents permission policies. The pages were accessed October 3, 2026.
- OpenAI developers: Agents SDK documentation on hosted MCP tool approvals and MCP authentication. The pages were accessed October 3, 2026.
- MIT AI Agent Index: The 2025 Index, reported in FAccT ’26 proceedings in June 2026.
Availability and controls can differ by product, account, workspace, and version; consult the relevant vendor’s current documentation for the exact setup you use.
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