Does an AI agent need its own computer?

Not always. If the work is repository-based or lives in supported connectors, a hosted cloud agent runs it without any machine of yours. An agent earns its own dedicated machine when the work needs persistent files, installed tools, long-running or scheduled jobs, and approved access to many client systems — the same reason an employee doing that work would need a computer set up for the role.

Does a dedicated agent machine have to be physically in your office?

No. The location is secondary. A dedicated agent machine can be a Mac mini in the office, a spare laptop, a Mac assigned to one agent, or a remotely hosted Mac or workstation your team administers. What matters is that the environment is persistent and under your control: the files, tools, runtime, workspace identity, and approved access survive between sessions and answer to you, not to a platform.

What persists between sessions in a hosted cloud agent?

It varies by provider, and it is mostly the output, not the environment. Codex cloud tasks run in containers whose state is cached for about 12 hours; Claude Code on the web provisions a fresh VM per session and expires idle environments; the GitHub Copilot cloud agent runs in an ephemeral GitHub Actions environment where nothing persists between tasks except commits and pull requests; Jules uses fresh VMs with optional environment snapshots per repository. Your repo, chat history, and configuration committed to the repo persist — the running environment generally does not.

Do cloud agents remove the need for workspace and tenant controls?

No. Cloud execution solves hosting and compute. It does not decide which client a task belongs to, which of many accounts behind the same tool is the right one, which credential alias is approved for that workspace, or how the action is audited per client. Those questions exist wherever the agent runs — on a hosted platform, on a Mac mini, or both — and they are answered by an access and policy layer, not by the choice of execution venue.

When is a hosted cloud agent enough on its own?

When the workflow is isolated and self-contained: the code lives in a supported repository, the data lives in supported connectors, the task fits the platform sandbox and its time limits, and there is no multi-client routing question. For a lot of development work — bug fixes, PR reviews, parallel refactors — a hosted cloud agent is genuinely the simpler and better answer.

Is a Mac mini a good dedicated machine for AI agents?

It has become a common choice: small, quiet, always-on, and cheap enough to dedicate to agent work rather than sharing a personal machine. Practitioners increasingly advise not running autonomous agents on your main computer. Any persistent machine your team controls works — the Mac mini is popular because it makes "give the agent its own computer" affordable, and macOS keeps credentials in a system keychain agents can be brokered against rather than handed.

Compare

Should your AI agents run in the cloud or on a dedicated machine?

Last updated: July 20, 2026

In short

A hosted cloud agent runs in an execution environment the platform provides and manages. A dedicated agent machine — local or remote — gives an agent a persistent working environment your team owns and controls.

They solve different problems. Cloud platforms provide managed compute, fast setup, and asynchronous execution in mostly ephemeral sandboxes. A dedicated machine provides persistent files, installed tools, and approved access under your control. Serious multi-client work usually ends up hybrid — and either way, something still has to decide which client's access each agent may use.

Open in: ChatGPT Claude Perplexity

This is not a "cloud bad, local good" argument. Hosted cloud agents are genuinely useful, and for plenty of work they are the right answer. But a cloud agent and a dedicated machine are not better and worse versions of the same thing — they differ in ownership, persistence, access, and control. Choosing between them (or combining them) starts with seeing that difference clearly.

What is a hosted cloud agent?

A hosted cloud agent executes inside an environment the vendor provisions, secures, and tears down. As of mid-2026 the main examples are OpenAI's Codex cloud tasks (isolated containers with your repo cloned from GitHub, container state cached for about 12 hours), Claude Code on the web (a fresh Anthropic-managed VM per session that expires after inactivity), Claude Cowork's remote execution mode (a temporary per-session sandbox, destroyed at session end), the GitHub Copilot cloud agent (an ephemeral GitHub Actions environment capped at 59 minutes per session), and Google's Jules (a fresh cloud VM per task, with optional per-repo environment snapshots). The pattern is consistent: the platform owns the computer; you bring the repository and the task.

A ChatGPT Project is a related but different thing: a persistent context workspace — chats, uploaded files, instructions, project memory — not a computer with installed tools. Persistent context is not a persistent working environment.

What is a dedicated agent machine?

A dedicated agent machine is a persistent computer assigned to agent work and administered by your team: an office Mac mini, a spare laptop, a Mac assigned to one agent, or a remotely hosted Mac or workstation you control. The physical location is secondary. What defines it is persistence and ownership — the project folders, installed software, runtime configuration, workspace identity, and approved API access are still there tomorrow, and every one of those things answers to you rather than to a platform's sandbox policy.

This is why the idea keeps resurfacing across the industry — from "give your agent its own computer" to the wave of always-on Mac minis quietly running agents around the clock, and the increasingly common operator advice not to run autonomous agents on your main machine.

The employee-computer test

When you hire an employee, you give them a computer: the tools installed, the folders they need, the accounts and access their role requires. A serious AI agent doing sustained, real work often needs the same thing — a stable environment assigned to its job. That environment can be a machine on a desk or a machine in a rack; an agent is not an employee, legally or operationally, but the operating logic transfers. If the agent's work would require a set-up computer when done by a person, it probably requires a persistent environment when done by an agent. If the work would fit in a borrowed browser session, a hosted sandbox is likely enough.

Side by side

Verified against official vendor documentation, July 20, 2026. Neither column is universally better — they are different ownership and persistence models.
Dimension	Hosted cloud agent	Dedicated machine (local or remote)
Where execution happens	Vendor-managed sandbox, container, or VM	A computer your team owns or rents and administers
Environment persistence	Mostly ephemeral: fresh per task/session, limited caching (e.g. ~12h Codex container cache, ~7-day Claude Code snapshot cache, per-repo Jules snapshots)	Persistent: files, tools, and configuration survive between sessions
Local / client files	Generally not available; work happens on a repo clone or supported connectors	Directly available in project folders the agent works in
Installed tools & runtimes	What the platform image plus setup scripts allow, within its limits	Anything you install and maintain
Background & scheduled work	Strong for asynchronous, parallel, repo-scoped tasks; session/time limits apply (e.g. 59-min Copilot cloud agent sessions)	Strong for always-on, long-running, scheduled multi-step workflows
Credential model	Varies by platform: setup-phase-only secrets (Codex), a dedicated agents secrets store (Copilot), no dedicated secrets store yet (Claude Code on the web, per its docs)	Your model: OS keychain or vault, ideally brokered so agents use access without seeing raw secrets
Client / tenant separation	Not a native concept — isolation is per task or session, not per client relationship	Not automatic either — a computer alone does not know which client is which; policy has to add it
Maintenance & uptime	Vendor-managed; near-zero operations burden	Yours: updates, uptime, device security, backups
Elastic scale	Strong — parallel tasks on demand	Limited by the hardware you dedicate
Setup effort	Minutes: connect a repo and go	Real: provision, configure, and keep the environment healthy
Best fit	Isolated, repo- or connector-based tasks; burst work; no private local runtime needed	Sustained multi-tool client work: persistent files, installed software, broad approved API access, many clients

What cloud platforms solve well

Credit where due: hosted cloud agents remove the whole category of "keep a machine alive" problems. Setup is minutes, not an afternoon. Compute is elastic — you can fan out parallel tasks without buying hardware. Execution is asynchronous and reachable from any device. The sandboxes are seriously engineered: Codex cloud tasks disable agent-phase internet access by default and strip secrets before the agent phase begins; Claude Code on the web routes GitHub access through a proxy so credentials never enter the VM; Cowork's remote sandboxes keep connector tokens out of the sandbox entirely; the Copilot cloud agent runs behind a default-on firewall. For repository-based development work, this model is excellent.

What they do not automatically solve

The platform controls the execution environment, so the environment ends where the platform's support does: supported repositories, supported connectors, the tools its image and time limits allow. Client files that live on your machines or in your Drive structure are not just there. The environment is mostly gone when the task ends. And most importantly for agencies: the platform has no concept of your client roster. Cloud execution solves hosting and compute. It does not decide which client a task belongs to, which of five ad accounts behind the same tool is the right one, which credential alias that workspace is approved to use, or how the action is audited per client. Those are your questions wherever the agent runs.

The multi-client problem is the real fork in the road

An agency running agents for many clients manages Google Ads and Meta accounts, Drive environments, CRMs, analytics properties, client media, and client-specific API credentials. For every task, the agent must land on the right client, the right account within that client, the approved resource, the allowed credential, the permitted action — and leave an audit trail. A dedicated machine alone does not solve this: it gives the agent a place to work, not a policy about whose work it is. A cloud sandbox alone does not solve it either: isolation per task is not separation per client. This routing-and-approval layer is its own thing — see managing API keys across many agent workspaces and wrong-client access in agent loops.

The hybrid pattern (where this is heading)

The vendors themselves are building the bridge: Codex moves chats and Git state between your computer and the cloud; Claude Code can start sessions in the cloud from the terminal, pull a cloud session down to local, or run on your own hardware monitored from the web (Remote Control); Cowork runs remote by default but keeps a local desktop mode. The emerging operating model — not yet universal, but visible in current market direction — is cloud intelligence and elastic execution, plus a persistent machine the team controls, plus an access and policy layer deciding what every agent, in either venue, may actually touch. Isolated burst work goes to the cloud; sustained client work lives on the dedicated machine; the access layer keeps both honest.

A practical decision framework

Use a hosted cloud agent when

→the workflow is repository-based, or the files already live in supported cloud systems;
→the platform's connectors and sandbox limits cover the task;
→the task is isolated and benefits from parallel, elastic execution;
→no private local runtime or persistent file state is required.

Use a dedicated machine when

→the agent needs persistent project folders and installed software;
→workflows span many tools, run long, or run on schedules;
→the agent needs broad approved API access across client systems;
→the runtime must stay under your team's control.

Use both when

→cloud execution is valuable for burst and repo work, but private or persistent resources are still required;
→the team needs one policy and audit story across both environments.

Where Outloop fits

A dedicated machine gives an agent a place to work. It does not automatically determine which client, account, credential, or resource the agent may use. Outloop provides that runtime access and workspace-control layer for multi-client agent environments: credentials stay in the Mac Keychain or another approved backend, agents request approved access without seeing the raw secret, access is granted to the correct workspace, tenant and resource boundaries are enforced, runtime use is audited, and wrong-client execution is reduced. Approved access can be reused across supported agent runtimes — see what is agent runtime access and the security model.

To be precise about where it runs: Outloop works where the Outloop Mac application, the approved credential backend, the agent runtime, and the project environment are available — your Mac, an office Mac mini, a Mac assigned to an agent, or a remotely hosted Mac your team controls. It does not run inside third-party hosted agent sandboxes operated by OpenAI, Anthropic, GitHub, or Google, and it is not a Mac hosting provider, a remote desktop product, or a vault. Runtime support varies by approved setup and proof state.

What happens when an agent on a dedicated machine requests client API access

01

Agent request

The agent asks for an approved action or alias — not a raw key.
02

Policy & tenant check

Outloop checks project, tenant identity, and runtime policy before anything runs.
03

Local broker

On approval, the local broker uses the credential on the wire to perform the call.
04

Redacted result

The agent receives a sanitized, non-secret result. Raw values never enter its context.
05

Audit log

Every attempt is written to a redacted local audit — decision, tenant, service.

The agent never sees the credential. A wrong-tenant request is denied at the policy check, before any backend call.

Sources

Platform claims verified against these official pages on July 20, 2026. These products change quickly — if you are reading this later, check the primary sources:

Related: Claude Cowork vs Claude Code, sandboxed Cowork agents + Google Drive, Outloop for AI agencies, and Outloop is not a vault. Outloop is available with guided onboarding for agency teams; the local app is verified on the founder's Mac; Apple signing/notarization and second-machine reproduction are still in progress.

The machine is the easy part. The access is the layer.

Wherever your agents run, Outloop controls which client workspace, account, API, and resource each one may use — without exposing the raw credential.

Start 14-day guided trial Book Access Teardown

Independent tools. Codex and ChatGPT are products of OpenAI; Claude Code and Claude Cowork of Anthropic; Copilot of GitHub; Jules of Google. Names belong to their respective owners. Outloop is not affiliated with or endorsed by these companies.