Choose a desktop AI agent by following the data and action paths, not by trusting labels such as “local,” “desktop,” or “self-hosted.” Identify where inference runs, where prompts and files are stored, where computer actions execute, which permissions are granted, and whether the agent supports your operating system and applications. Then run a controlled trial on low-risk tasks with observable completion checks.
These distinctions matter because a desktop interface does not prove local processing. OpenClaw can be self-hosted and configured with local models, yet configured provider and chat-platform traffic still goes to those services. Claude Cowork runs tasks in Anthropic’s cloud while reaching approved local folders or applications through its desktop app. ChatGPT Work coordinates synced tasks in the cloud, even when a connected computer performs a local step.
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Start by mapping the complete data path
Before comparing products, draw the route taken by every sensitive item in your workflow:
- Prompts and conversation history
- Documents, folders and application data
- Screenshots and screen contents
- Tool results, scripts and generated files
- Credentials, API keys and session tokens
For each item, ask where it is processed, where it is stored, who can access it, and how long it remains available. “Desktop app,” “local storage,” “local inference” and “local execution” describe different properties. An agent can have one without the others.
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Inference location is not execution location
Inference is the model’s generation of a response or plan. Execution is the later action: opening an application, moving a file, entering text or running a tool. A local model may generate a plan on your computer while a cloud service executes a related task, or a cloud model may direct an action that occurs on your computer. Treat those as separate decisions.
Self-hosting has boundaries
OpenClaw documents a self-hosted Gateway and support for local models. It also states that traffic for the providers and chat platforms you configure goes to those providers and platforms. Its state directory can contain channel credentials, model-authentication data, session transcripts and tool output. Self-hosting therefore reduces dependence on a hosted control plane, but it does not automatically keep every prompt, message or tool result on one machine.
Compare the main operating models
| Option | Where model or task work occurs | How local resources are reached | Requirements and boundaries |
|---|---|---|---|
| OpenClaw | Self-hosted Gateway; local models are supported, while configured provider and chat traffic goes to those services. | Through the Gateway and its configured tools and channels. | Owner manages host access, credentials and the protected state directory. Setup checks the selected model connection with a live completion. |
| Claude Cowork | Tasks execute in an isolated Anthropic-server environment. | The open Claude Desktop app can provide approved local folders, browser access and computer use. | Paid Claude plan, macOS or Windows desktop app and an active internet connection. The app must remain open for the session to reach local resources. |
| ChatGPT Work | Synced work is coordinated in the cloud. | A connected computer performs local steps when the workflow requires it. | Local work sync is an optional workspace capability. The documentation says Zero Data Retention is not supported for synced Work tasks; it does not describe Codex workflows. |
This table is a starting map, not a privacy ranking. A cloud-executed agent may be acceptable for a particular folder, while a self-hosted agent may be risky if its host, tokens or logs are poorly protected.
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Evaluate privacy and permission boundaries
List what the agent can see
Make a written inventory before granting access. Include folder paths, browser profiles, applications, network destinations, clipboard contents, screenshots and any account used to authenticate tools. A broad “computer access” grant can expose information that was never part of the original task.
Understand computer-use visibility
Claude Cowork asks before accessing applications. Its computer-use feature takes screenshots and can see information visible on the screen. A password manager window, message notification or unrelated document may therefore become part of the agent’s working context. Anthropic’s official guidance says, “Claude asks for your permission before accessing each application.” Permission prompts are useful boundaries, but they do not make every visible item safe to expose.
Assume safeguards can fail
Anthropic also states, “These safeguards aren’t perfect, and Claude may occasionally act outside these boundaries.” Its safety guidance specifically discusses prompt injection in websites, emails and documents. Treat instructions encountered inside untrusted content as data, not as authority. Limit connected folders, use trusted web access where available, and keep high-impact actions behind a human confirmation.
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Protect self-hosted state
With OpenClaw, the state directory is a security boundary. Protect it with operating-system permissions, restrict host access, and include it in your backup and incident-response plan. A copied state directory could contain channel credentials, model-authentication data, transcripts and tool output. Decide who can read backups and whether old transcripts should be deleted before you connect real accounts.
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Check retention terms for cloud coordination
For ChatGPT Work, the documented Zero Data Retention option is not supported for synced Work tasks. If your organization requires that control, exclude those tasks from Work sync or choose an architecture whose retention terms meet the requirement. Do not infer that a local step changes the retention policy for the cloud-coordinated task.
Test compatibility against the exact workflow
Compatibility is more than operating-system support. Verify the application, connector, account, model and network conditions for every step you expect the agent to perform.
Use this compatibility checklist
- Operating system: Confirm whether the required desktop app supports your edition of macOS or Windows.
- Application: Check that the agent can connect to the exact browser, editor, terminal, file manager or business application you use.
- Account: Determine which account must be signed in and whether multifactor authentication or an administrator approval interrupts automation.
- Model: Verify that your selected provider or local runtime is supported and authenticated.
- Network: Establish whether an active internet connection is required for planning, execution or both.
- Connector scope: Confirm which folders, channels and devices are exposed, and whether access can be revoked individually.
- Device state: Check whether the computer must stay awake, unlocked or connected during a task.
Validate Claude Cowork prerequisites
Claude Cowork’s documented local computer, browser and file access requires the Claude Desktop app on macOS or Windows and an active internet connection. Task execution occurs on Anthropic’s servers, and the desktop app must be open so the session can reach local resources. If the app is closed, the computer is offline or the required folder was not approved, a cloud task cannot complete the local step.
Validate an OpenClaw setup
OpenClaw setup connects a local Gateway and checks the selected model connection with a live completion. Treat that completion as a connection check, not proof that your full workflow works. Confirm every channel and tool separately. On macOS, review the capabilities exposed by the documented app before enabling them; the model connection succeeding does not mean that file, browser or device actions are configured safely.
Run a preflight in the right order
- Write the workflow as individual actions, such as “find the latest invoice,” “extract the total,” “place the value in a spreadsheet” and “save a copy.”
- For each action, record the required application, folder, account, model and network state.
- Connect only a test folder and a non-sensitive account.
- Complete the product’s model or provider connection check.
- Run one action at a time and confirm the observed result before adding the next action.
- Remove or revoke unused connectors after the test.
Judge reliability with task-level evidence
Reliability means completing your task correctly, under your configuration, with an output you can verify. It is not the same as fluent answers, a successful demonstration or a high-level product reputation.
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What the available benchmark does—and does not—show
MacAgentBench reports a 73.7% Pass@1 result for its best evaluated configuration: Claude Opus 4.6 running on OpenClaw. Pass@1 means the task passed on the first attempt in that benchmark setup. The authors report that the advantage was primarily driven by the skill library rather than framework design. The result comes from a specific real-world macOS task set and does not provide a universal success rate, a guarantee for Windows, or a ranking across every desktop workflow.
There is no verified head-to-head adoption figure, incident rate or cross-workflow reliability statistic in the available material. Use the benchmark as a signal that configuration and reusable skills matter, not as a promise about your own results.
Define observable completion checks
Before a trial, state what “done” means in a way another person could verify. Examples include a file appearing at an exact path with a specified name, a spreadsheet cell matching a source value, or a draft being saved without sending it. Record both the final artifact and important intermediate actions. A polished explanation is not evidence that the action occurred.
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- Choose three to five representative tasks with low business and personal risk.
- Use synthetic documents or a limited folder instead of production data.
- Start read-only where possible; require confirmation before sending, deleting, purchasing or changing permissions.
- Observe screenshots, tool calls, file changes and generated output as the task runs.
- Mark each task pass or fail against the predefined completion check, and record why a failure occurred.
- Repeat a task after correcting the prompt, permission or connector so you can distinguish a one-off error from a systemic limitation.
- Expand access only when the task genuinely needs it, then repeat the same checks.
This protocol is practical guidance derived from the documented permission and safety behavior; it is not an independently validated testing standard.
Understand each option before adopting it
OpenClaw: control with operational responsibility
OpenClaw is the better fit when you want to operate the Gateway yourself, choose among model providers or investigate a local-model path. Its model-agnostic design and documented detection of runtimes such as Ollama, LM Studio and llama.cpp provide flexibility. That flexibility transfers responsibility to you: host hardening, updates, backups, token rotation, transcript retention and network exposure all become part of the deployment.
Decide where the Gateway runs, who can reach it and how its state directory is protected before connecting chat channels or real credentials. If you select a hosted provider, document exactly which prompts, messages and tool results leave the host. If you select a local model, still check whether a configured channel or integration sends data elsewhere.
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Claude Cowork: cloud execution with an explicit local bridge
Claude Cowork is designed for users who accept Anthropic-server task execution but need the agent to work with selected local files, a browser or desktop applications. The bridge is conditional: the Claude Desktop app for macOS or Windows must be open, the computer must be online, and the relevant access must be approved. Screenshots and visible screen content are part of the computer-use model, so arrange the desktop to hide unrelated confidential information before granting access.
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Use its permission prompts as checkpoints rather than blindly approving a complete session. Keep connected folders narrow and separate drafting from irreversible actions. Anthropic’s warning that safeguards are imperfect is particularly relevant when a task reads websites, email or documents that may contain malicious instructions.
ChatGPT Work: cloud-synchronized workspace tasks
ChatGPT Work’s local work sync is an optional workspace capability. The task is coordinated in the cloud, while a connected computer performs local steps. This can suit teams that already operate inside that workspace model, but it should not be treated as an all-local mode. The documented Zero Data Retention limitation applies to synced Work tasks, and the documentation does not describe Codex workflows; do not generalize the Work behavior to another product or workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose by risk and operating model
Prefer a self-managed path when control is the priority
Choose a self-hosted design only if your team can manage its host, credentials, logs and updates. Local inference can reduce dependence on a model provider, but it does not remove the need to audit channels, integrations and backups. The decisive question is whether you can operate the entire data path safely, not whether the interface says “local.”
Prefer cloud execution when convenience outweighs local processing
A cloud-executed agent can be practical when you need a maintained service and are comfortable with the provider processing task context. Limit the folders and applications it can reach, understand retention terms, and keep high-impact actions confirmable. Internet availability and an open desktop bridge may be hard requirements rather than optional conveniences.
Prefer the narrowest permission set that completes the job
Start with read-only access and a test directory. Add browser, application or write permissions one at a time. If a task can be completed by importing one file, do not expose an entire drive. If a draft can be saved for review, do not authorize sending it automatically.
A practical decision sequence
- Classify the data: Separate public, internal, personal and regulated information.
- Draw the path: Mark where inference, storage and execution occur for each candidate.
- List required actions: Include every application, folder, browser session, account and device capability.
- Check hard prerequisites: Confirm operating system, desktop app, internet, model connection and connector availability.
- Set permission limits: Use the smallest folder and application scope, with confirmation for irreversible actions.
- Run representative trials: Measure observable outputs, not conversational quality alone.
- Review operations: Protect state directories, rotate credentials, decide retention and document revocation steps.
- Expand cautiously: Increase permissions only after the limited configuration passes your checks.
Common failure modes and recovery
The agent cannot reach a local file
Check that the desktop bridge is open, the computer is online, the correct folder was approved and the file path is inside the approved scope. For a self-hosted Gateway, verify the host process and connector permissions separately from the model connection.
A model connection succeeds but the task fails
A live completion proves that the selected model can respond; it does not prove that the browser, application, account or file tool is usable. Test each connector with a harmless action and inspect the resulting permission prompt or error.
The agent follows instructions embedded in a document or website
Stop the run, treat the content as a prompt-injection attempt, and remove access to the affected source. Re-run with a narrower folder or trusted site list and require confirmation before any external or destructive action.
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Close unrelated windows, hide notifications, use a sanitized test account and restrict the task folder. Review transcripts, tool output and state storage afterward; rotate credentials if they may have been exposed.
A task reports success but the artifact is wrong
Use an objective completion check, compare the output with the source, and inspect intermediate actions. Correct the permission, skill or prompt only after identifying which step failed, then repeat the same task under the same check.
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