AI agents interact with apps through tools the app or a connecting service makes available. The model can propose an operation, but that does not give it access by itself: a connected identity determines what data it can reach, and the host or provider can allow, require approval for, or block the action. With an API integration, the agent requests a defined operation; with computer use, it operates the app’s interface through clicks, keystrokes, and other UI actions.
How do AI agents interact with apps?
The interaction is a loop between the model, the system hosting it, and the app. A model response is a proposed action—not proof that the action is authorized or has happened.
- The host exposes available actions. An app, integration, or MCP server may offer tools such as searching records or creating an item. The model can choose among the actions it has been given; it cannot call an unexposed tool merely by describing it.
- The model proposes an operation. For a tool call, this is typically a structured request naming the tool and supplying its inputs. For computer use, it may be a proposed click, scroll, or keystroke based on a screenshot.
- The host checks policy and authorization. It determines whether the action is permitted in that session and whether approval is needed. Separately, the connected account’s credentials and provider permissions determine which resources the request can access.
- A client or runtime executes the permitted action. An API client sends a request to a backend operation. A computer-use client carries out the UI action in the target environment.
- The app returns a result. It may return structured data or an updated screen. The agent can use that result to decide whether to stop, ask a question, or propose another action.
If a request is denied, requires approval, or fails, the loop may stop or take another path. The model’s ability to reason about an action does not bypass these checks.
What permissions actually control
“Permission” can refer to several separate controls. They work at different points in the chain, so a user’s approval of one action is not the same as granting an integration broad access to an account.
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Identity and provider authorization set the access boundary
The identity used for a connection determines whose access is being exercised. If an integration acts with a user’s identity, the user’s existing resource permissions can govern what it may access. Google Cloud says that actions through its MCP servers using a user’s identity are attributed to that user and have the same resource permissions as that user.
For production systems, Google recommends a separate agent or workload identity with only the permissions it needs, along with logging. Its documentation 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.
MCP is a protocol for connecting a client to a server that provides tools; using MCP does not automatically grant access to a whole account. The server still authenticates the client, and the identity and permissions behind the connection determine the accessible resources. OpenAI’s MCP authentication guidance describes protected-resource and authorization-server metadata, scopes, a resource parameter, and an authorization-code flow using PKCE with the S256 challenge. Those are implementation details, not a guarantee that every MCP product uses the same flow. Developers also need to plan for token revocation, refresh, and scope changes.
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Host policy controls which actions are exposed or allowed
A host can restrict the available actions independently of the connected identity. For example, OpenAI’s Agents SDK documentation describes allowlists for hosted MCP tool names and approval policies, including per-tool settings. Anthropic’s Managed Agents permission policies describe allow, ask, and deny outcomes for server-executed agent and MCP tools. These are examples of product-specific controls, not one universal permission system.
Approval is a checkpoint, not an access grant
An approval prompt answers whether an available operation may run in a particular host or session. It does not, by itself, expand the connected account’s provider permissions. Conversely, a user may have authorized an integration with a provider while the host still blocks a particular action or pauses to ask first.
ChatGPT’s app-permission documentation separates provider authorization, action controls, workspace app settings, role controls, and app permissions; the controls available can 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 at the provider. In Anthropic’s documented auto path, a server-denied call cannot be overridden by user confirmation.
API or tool call versus computer use
Both approaches can let an agent work with an app, but they reach it differently. An API or MCP tool call targets a defined operation; computer use interacts with the app as a person would through its visible interface.
| What differs | API or MCP tool call | Computer use |
|---|---|---|
| Action surface | Named, structured operations exposed by an API or tool server. | Visual actions such as clicks, scrolling, and keystrokes, chosen from the displayed interface. |
| How the agent acts | The model returns a structured request; a host or client checks it and sends it to the relevant backend. | A client provides the model with a prompt and screenshot, executes permitted UI actions, then sends back the updated state. |
| Access boundary | The identity, token scopes, provider permissions, and host tool policy constrain what the operation can do. | The target environment and account still matter, while host policy and the client’s action handler determine whether proposed UI actions run. |
| What a result looks like | Often structured data returned by the operation. | A new screenshot or other state captured from the environment. |
| Human involvement | May run automatically, ask for approval, or be denied, depending on the product’s configuration. | May also be subject to runtime controls or confirmation; there is no single default across products. |
| Typical trade-off | More explicit operation boundaries, if the integration exposes narrowly scoped tools. | Can work through visible screens, but depends on interpreting and acting on a changing interface. |
The trade-off is not simply “safe API, risky screen.” A broadly privileged API can still make consequential changes, and a screen action can be constrained by its runtime. What matters is the combination of identity, available actions, policy, approval, execution environment, and the consequences of mistakes.
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Google’s Gemini API Computer Use documentation describes a repeated screenshot-and-action loop: the client sends a prompt and screenshot; the model returns a suggested function call such as a click, scroll, or keystroke; client-side code executes an allowed or user-confirmed action in the target environment; then the client captures the updated state and continues. The model suggests the UI action, while the client-side handler performs it.
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Google recommends running computer use in a sandboxed virtual machine or container and using a client-side action handler. Anthropic likewise describes its computer-use tool as a client toolset: the application runs each call in an environment it controls, sends actions to that environment, and returns results. For tasks limited to webpages, Anthropic says its browser-use tool is a closer fit than whole-desktop computer use.
Tool names, supported models, versions, and availability can change. Google’s computer-use feature is described as preview, and its guidance recommends close supervision for important tasks. Google advises against using it for critical decisions, sensitive data, or situations where a serious mistake cannot be corrected.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this means when an agent can reach a financial app
For a finance-related app, think about the identity and the effect of an action—not just whether the agent can see a screen. A connection acting as you may operate within your account’s existing permissions; a confirmation prompt does not necessarily narrow those underlying permissions. The exact actions available depend on the app, integration, connected identity, and host configuration.
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- Check the connected identity. Find out whether the integration acts as your user account or a separate service identity, and which account is connected.
- Review the granted access. Look for the provider’s authorization details and the host’s app or tool controls. Do not assume that approving one request is the same as authorizing an account connection—or that changing a host setting revokes provider access.
- Separate reading from changing records. Where the product permits it, prefer the narrowest tools and permissions needed. Consider the consequences of actions that submit, edit, or delete information, rather than treating all tool calls alike.
- Keep consequential actions reviewable. Use approval checkpoints where available, and supervise actions whose errors would be difficult to reverse. An approval mechanism is useful only if the action and its effect are clear before it runs.
- Know how to stop future access. Identify how to disconnect the account or remove the provider authorization. Changing an in-host permission alone may not revoke an authorization already granted to the app provider.
These are general safeguards, not a claim that any particular financial app supports a particular agent integration or permission setting. Check the app and provider’s current documentation before connecting an account.
How common are these interaction patterns?
The MIT AI Agent Index reported that 20 of the 30 indexed agents supported MCP for tool integration, while all 5 indexed browser agents manipulated web pages through click, type, or navigate actions. These are counts within the Index’s documented sample—not market-share figures or a census of deployed agents. The report appeared in the FAccT ’26 proceedings in June 2026.
Product controls and availability vary. The vendor documentation described here was accessed on October 3, 2026; Google’s MCP documentation identifies an update dated September 30, 2026. Check the relevant provider’s current documentation for the product, account, and plan you use.
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