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AI agents interact with apps through actions that a host makes available and authorizes—not through reasoning alone. For an API or MCP integration, the agent proposes a structured operation that a client can check and send to a service. With computer use, the agent interprets a screenshot and proposes clicks or keystrokes that a client may execute in an app. In both cases, the connected identity, host policy, and application determine what can actually happen.
How do AI agents interact with apps?
An app interaction is a chain of separate decisions and events. The model can choose or suggest an action, but it does not automatically possess the credentials or authority to carry it out.
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- Tools or actions are exposed. A host, app, or MCP server gives the agent a set of possible operations, such as searching records or sending a message.
- The model proposes an action. It returns a structured tool request or, for computer use, a suggested UI action based on a screen image.
- The host checks policy and authorization. The client may allow the action, require approval, or deny it. Provider permissions and the connected identity determine which underlying resources are accessible.
- A runtime executes the action. It sends an API request to a service or carries out an allowed UI action in the target environment.
- The app returns a result. The client sends the response or updated screen back to the model, which can then decide whether another action is needed.
These stages matter because a model’s proposed action is not proof that the user or agent is authorized to perform it. A host can restrict which tools are available, while the service can separately reject a request because the identity lacks access.
What do permissions control?
“Permission” can refer to several controls that operate at different layers. An approval prompt is not the same thing as a provider granting an identity access to data.
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- Provider authorization: The service decides what the connected account or service identity can access. OAuth scopes can limit the kinds of access a user authorizes; the identity’s actual resource permissions also matter.
- Host or app policy: The agent product can expose an allowlist of tools, require confirmation for certain actions, or deny actions. The available controls and defaults differ by product.
- Workspace and role controls: An organization may restrict which apps or capabilities are available to a user. Those restrictions can apply in addition to the provider’s own authorization.
- Session approval: A host may pause before an action runs and ask a person to approve it. Approval governs that action in that context; it does not grant broader access to the connected account.
For example, ChatGPT’s documented app settings distinguish provider authorization, app permissions, action controls, workspace settings, and role controls. The exact settings vary by account, app, connected account, and workspace. Changing an app permission does not itself disconnect the account or revoke access already granted by the provider; stopping future access may require disconnecting the account or unlinking it with the provider.
Other products implement policy differently. Anthropic’s Managed Agents permission policies document allow, ask, and deny outcomes for server-executed agent and MCP tools. In the documented auto path, a server-denied call cannot be overridden by a user confirmation. OpenAI’s Agents SDK documents configurable approval requirements and callbacks for hosted MCP tools. These examples are product-specific, not one universal permission standard.
How does an API or MCP integration work?
An API or tool integration exposes defined operations rather than asking the agent to operate an app’s visible interface. The model returns a structured request—such as a request to look up an item—and a client or host decides whether it may invoke the relevant backend operation.
MCP is one protocol route between a client and a server that provides tools. Using MCP does not, by itself, give an agent access to an entire account or make every server tool available. The server authenticates the client, and the identity or token used for the connection determines which resources a request can reach.
Whose identity is used?
Depending on the service and setup, a connection may use a user’s identity, a workload or agent identity, or—in services that do not require an IAM principal—an API key. Google Cloud documents these options for its remote Google and Google Cloud MCP servers. When an MCP action uses a user’s identity, Google says the action is attributed to that user and has the same resource permissions as that user.
That can make an agent’s successful action equivalent to an action available to the connected user. For production systems, Google recommends a separate agent or workload identity, minimum necessary permissions, and IAM attributes to restrict read or write tool use on important resources. An OAuth client uses the scopes the user authorizes; the AI application does not receive the user’s raw credentials.
What does OAuth add?
OAuth is an authorization mechanism, not a guarantee that an agent product supports every possible scope or flow. 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 evidence that every MCP-capable product uses the same flow or offers identical controls.
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How does computer use work?
Computer use follows a visual feedback loop. The client supplies a screenshot and prompt; the model proposes an interface action; client-side code may execute an allowed or user-confirmed action in the target environment; then the client captures the changed screen and sends it back for the next decision.
Google describes its Gemini API Computer Use flow this way: “The model analyzes the screen and the prompt, returning a response which includes a suggested function_call representing a UI action (such as a click, scroll, or keystroke).” The suggestion is not execution by itself: client code handles the action and the resulting screen state. Google recommends using a sandboxed VM or container and a client-side action handler.
Anthropic describes its computer-use tool in a similar client-tool pattern: the application runs each call in an environment it controls, performs the loop of sending actions to that environment, and returns results. For work limited to webpages, Anthropic says its browser-use tool is a closer fit than whole-desktop computer use.
Computer use can reach interfaces that lack a direct integration, but it depends on interpreting visible state and interacting through controls intended for people. As of Google’s documentation accessed on October 3, 2026, Gemini Computer Use was described as a preview. Google recommends close supervision for important tasks and advises against using it for critical decisions, sensitive data, or tasks where a serious mistake cannot be corrected. Tool names, model support, availability, and preview status can change; check the relevant vendor documentation for the product and account in use.
API/tool calls vs. computer use
| Question | API or MCP tool | Computer use |
|---|---|---|
| What does the agent act on? | Defined operations exposed by an API or MCP server. | A visible app or desktop interface, using actions such as clicks, scrolling, and keystrokes. |
| How is an action represented? | A structured request naming an operation and its inputs. | A suggested UI action based on a screenshot and prompt. |
| Who executes it? | A host or client invokes the backend after its policy checks. | Client-side code performs the action in the target environment and returns an updated screen. |
| What determines access? | The connected identity, token or scopes, service permissions, and host policy. | The environment and account being operated, plus client safeguards and the applicable app or service access. |
| Where can it fit? | Tasks covered by exposed, defined operations. | Tasks that can be performed through a visual interface, including interfaces without a suitable tool integration. |
| What oversight is appropriate? | Depends on the operation, host policy, and consequences; products may allow, pause for approval, or deny. | Google advises close supervision for important tasks during its documented preview and cautions against critical or sensitive use where errors cannot be corrected. |
What should teams check before connecting an agent?
- Identify the principal. Confirm whether actions run as a user, workload, agent identity, or API key, and whose name will appear in service records.
- Limit the available operations. Expose only the tools needed for the task; use per-tool allowlists or policies where the host supports them.
- Separate approval from access. Decide which actions need human confirmation, while separately checking provider scopes, resource permissions, and workspace restrictions.
- Consider impact and reversibility. A read-only lookup has a different consequence from deleting records, sending communications, or changing access. Require stronger checks for sensitive or hard-to-reverse actions.
- Use a controlled runtime for computer use. Follow vendor guidance for a sandboxed VM or container, and supervise tasks whose errors could have serious consequences.
- Plan for identity changes. For OAuth or token-based integrations, understand how access is refreshed, revoked, or changed when scopes or permissions change.
- Check audit attribution. Verify whether activity is recorded under a user or service identity and whether the resulting records are sufficient for your operational needs.
How common are these interaction patterns?
The MIT AI Agent Index’s documented sample in 2025 listed MCP support for tool integration in 20 of 30 indexed agents, and click/type/navigate manipulation of web pages for all 5 of 5 indexed browser agents. These are counts within that Index’s sample, not market-share estimates or a census of deployed agents. The report appeared in the FAccT ’26 proceedings in June 2026.
What this means in practice
For a well-defined task, an API or MCP tool can provide a structured route to an operation, but its authority still depends on the identity, service permissions, and host policy. Computer use provides a route through the visible interface, but it requires a runtime to carry out actions and a feedback loop to check the screen afterward. In either case, treat the model as proposing what to do; the host, identity, and app determine what can run.
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