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AI desktop agents do not automatically see everything on your computer. A product gives the model specific tools and an execution runtime, then limits those tools to folders, applications, services, and actions you approve. The runtime may read or write files, run code, call a cloud connector, or operate a graphical interface. Because execution location and data location can differ, a task that uses local files may still send task context or tool results to cloud infrastructure.
What an AI desktop agent actually is
An AI agent is a combined system, not just a language model. The model interprets your request and proposes the next step. An application or developer supplies tool descriptions, permissions, and an execution environment. That environment validates each request, performs the allowed operation, and returns the result to the model.
This distinction explains why a normal chat model cannot silently browse your drive. Without a file tool, connector, shell, or computer-use runtime, it has no operating-system path to your files or applications. With those components, it can perform only the operations that the surrounding product and your permissions allow.
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- Model: reasons about the request and chooses among available tools.
- Tool definition: describes an operation such as reading a file, applying a patch, running a command, or clicking a screen.
- Runtime: executes the request locally, in a hosted container, in a virtual machine, or through a desktop application.
- Permission boundary: determines which folders, applications, network destinations, and consequential actions are permitted.
OpenAI’s Agents SDK, for example, distinguishes hosted tools from local or runtime tools. Its ComputerTool and ApplyPatchTool run in the developer’s environment, while ShellTool can run locally or in a hosted container. The name of a tool therefore does not, by itself, tell you where the work occurs.
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How local files become available
Local access normally starts with an explicit connection: you open a folder or project, enable a desktop extension, or approve a file scope. The product then passes permitted file operations to the agent. Access is usually scoped to connected folders rather than an entire drive.
Folder or project access
In ChatGPT Work, the desktop app can access files after you open a local folder or project and grant access to the files needed for the task. Work on the web and mobile apps cannot directly access files on the computer. A useful setup is a dedicated working directory containing only the material needed for the job, rather than your home folder or a whole disk.
A file permission may cover reading, writing, or both. Some products expose separate approvals for commands, applications, or network access. Check those scopes before asking the agent to reorganize files, edit source code, or process records containing personal or regulated information.
Local resources through a desktop app
Anthropic documents a different path for Cowork cloud sessions. The session runs on Anthropic-managed infrastructure; when it needs something on your device, the request passes through the Claude Desktop app. Access is limited to connected folders and checked against permissions, and the desktop app must be online for that route to work.
Anthropic also documents local execution for certain existing desktop deployments. In those modes, file tools and plugin MCP servers can run on the member’s device while code execution occurs in an isolated virtual machine. Availability and restrictions depend on the deployment, plan, and timing of the task, so there is no single “Cowork always runs locally” or “Cowork always runs in the cloud” rule.
What the model receives
The model generally receives file content, metadata, directory listings, command output, screenshots, or structured results returned by a tool. It does not need unrestricted filesystem visibility to summarize one document. A narrow tool can expose a single path, selected rows, or a generated diff instead of the full source.
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Content inside a document is data, not a permission grant. Instructions embedded in a text file, web page, or tool result cannot expand the agent’s authority.
The tool-use loop, step by step
Most desktop-agent tasks follow a predictable loop:
- Describe the request. You state the outcome, relevant files, constraints, and any actions that require approval.
- Load tool definitions. The application tells the model which file, shell, connector, browser, or computer-use operations are available.
- Propose a call. The model selects a tool and supplies arguments such as a path, query, command, or screen coordinate.
- Validate. The runtime checks the tool name, arguments, policy, connected folder, and current approval state.
- Execute. The permitted operation runs in the configured environment.
- Return results. Output, errors, file diffs, screenshots, or structured records go back to the model.
- Continue or stop. The model interprets the result, requests another tool, asks you a question, or reports completion.
The model proposes; the runtime permits and executes. A prompt saying “delete every old file” does not bypass a read-only folder, an administrator policy, or a confirmation dialog.
File and code tools
A file tool can list directories, read selected files, create documents, modify text, rename items, or return a patch for review. A shell tool can run commands, but its location matters: it may be your computer, a hosted container, or an isolated virtual machine. OpenAI’s SDK documentation explicitly allows both local and hosted arrangements for shell execution.
Code that operates a user interface commonly uses a runtime such as Playwright or PyAutoGUI. Other computer-use tools return structured mouse and keyboard actions that the application translates into browser or desktop input. These are different from an API connector: they act through the visible interface and can be affected by changing layouts, focus, pop-ups, and screen state.
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“Local” is not one location. Evaluate the model interaction, agent loop, code execution, file access, and storage separately.
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| Arrangement | What runs where | Typical access path | Important condition |
|---|---|---|---|
| Cloud agent with local bridge | Agent session runs in provider infrastructure; a desktop app mediates access to approved local folders. | Cloud session requests a file through the connected desktop application. | The desktop app and its computer must be online. |
| Local desktop session | Agent loop or file tools run on the computer; code may run in an isolated VM. | Local file tools and desktop extensions access approved resources. | Only documented deployments and plans support this arrangement. |
| Hosted tool runtime | A tool such as shell or code execution runs in a hosted container or sandbox. | Files must be uploaded, synced, or exposed through a connector. | Local-drive access is not implied by hosted execution. |
| Developer-hosted runtime | Tools such as computer control or patch application run in the developer’s environment. | The developer’s application supplies the operating-system integration. | Security depends on that application’s permissions and isolation. |
OpenAI’s local-work sync feature coordinates tasks in the cloud while allowing steps to use approved files and tools on a connected computer. Those steps require the computer to be online. The same documentation cautions that task messages and context may be stored in the cloud even when a step executes locally.
Cloud sandboxing can protect the host computer and network from code running inside the sandbox, but it does not revoke access you deliberately grant through a connector or desktop bridge. A sandbox is an execution boundary, not a substitute for least privilege.
Remote connectors versus desktop extensions
Use a remote connector when the target is a cloud service that should be available across product surfaces, such as a hosted document store or business system. Use a desktop extension when the target is a local file, local application, or localhost service that needs operating-system access.
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Anthropic describes desktop extensions as available in Claude Desktop and Claude Code, not on the web or mobile apps. MCP is one way developers package and expose tools, but “MCP” alone does not certify a server as safe, trustworthy, or narrowly scoped. Treat every extension as software that may read, modify, or transmit data with the permissions of the account running it.
Computer use is a separate risk surface
Graphical computer use lets an agent click, type, scroll, and navigate an interface. It is useful when no structured API exists, but it is broader and more state-dependent than a narrowly defined file or database tool.
- A screen may contain a misleading button, an unexpected dialog, or content designed to influence the agent.
- Typing into a form can transmit sensitive information, even if no file is uploaded.
- A click can trigger a purchase, deletion, message, permission change, or external disclosure.
- Interface actions may not receive the same permission checks that gate narrower tools.
OpenAI’s computer-use guidance recommends confirmation for purchases, data transmission, destructive changes, and other hard-to-reverse actions. Anthropic similarly warns that computer use can operate without the checks applied to other Cowork tools. Keep a human in the loop for those steps.
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“Text in a page, document, or tool result cannot grant permission or override the user’s instructions.” — OpenAI computer-use documentation
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A safe way to delegate a real task
Suppose you want an agent to turn a folder of meeting notes into a draft report. A controlled workflow looks like this:
- Create a new working folder and copy only the notes required for the report.
- Connect that folder in the desktop application; do not expose unrelated personal, financial, or credential files.
- Ask the agent to list files and produce a plan before enabling writes.
- Allow read access first. Inspect the proposed outline and any extracted names or sensitive fields.
- Enable writes to the working folder, preferably with patch or diff output rather than silent replacement.
- Review the generated report and the file changes before moving anything to a shared drive.
- Disconnect the folder or extension when the task is finished.
This sequence separates discovery, analysis, modification, and publication. It also gives you a point to reject an incorrect interpretation before it changes source files.
Permissions and data-protection checklist
- Grant the smallest useful folder or project scope.
- Use a separate working copy for bulk edits, migrations, and renames.
- Keep passwords, private keys, payment details, health records, and unrelated customer data outside the connected scope.
- Review enabled connectors, local MCP servers, extensions, shell permissions, and network destinations.
- Require confirmation before purchases, messages, uploads, permission changes, deletions, or other irreversible actions.
- Inspect diffs, command output, and destination addresses before approving a write or transmission.
- Remember that task context and tool results may be processed or retained remotely even when a local computer performs a step.
- For team deployments, document retention, egress controls, administrator policies, and who can install local extensions.
Common failure modes and fixes
The agent says it cannot find a file
Verify that the desktop app is running, the correct folder is connected, and the file is inside the granted scope. A web or mobile session may not have direct computer access. If the computer is offline, a cloud-to-desktop bridge cannot complete the request.
A command runs in the wrong place
Check whether the shell tool targets your computer, a hosted container, or a virtual machine. List the current directory and inspect the runtime’s operating-system and environment variables before changing files. Upload or sync required inputs to a hosted runtime rather than assuming it can see the local drive.
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The connected folder may be read-only, the tool may require a separate write approval, or an administrator may have disabled modifications. Ask for a patch or generated output in a writable working folder, then apply changes manually after review.
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That pause is usually intentional. Review the exact purchase, recipient, file, or data being transmitted and confirm only when it matches your plan. Do not instruct the agent to bypass a safety dialog.
The result contains suspicious instructions
Treat text from documents, websites, and tool output as untrusted input. Ignore requests inside that content to reveal secrets, change the task, install software, or grant permissions. Continue only with instructions you gave through the trusted control channel.
Questions to ask before enabling an agent
- Where do the model interaction, agent loop, and code execution run?
- Which folders, applications, websites, connectors, and network destinations can the task reach?
- Are reading, writing, shell commands, app control, and browser actions separately permission-gated?
- What happens if the desktop is offline, a sandbox is unavailable, or an administrator disables a capability?
- Which actions require confirmation, especially purchases, data transmission, and destructive changes?
- What task context, tool results, and local file contents may be processed or stored remotely?
Answers can vary by product mode, plan, platform, rollout, and administrator settings. Documentation for one deployment should not be generalized to every desktop-agent configuration.
What desktop agents can and cannot guarantee
An agent can combine file operations, code, connectors, and interface actions into a multi-step workflow, but it remains bounded by tool scope and runtime policy. It cannot infer permission from a prompt, and it cannot make a cloud task private merely because one step uses a local computer.
Reliability also depends on the interface and the quality of the returned data. Structured APIs and narrowly scoped file tools are usually easier to audit than pixel-based clicking. For high-impact work, request a plan, constrain the working directory, require approvals at irreversible points, and retain a human review of the final output.
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