AI agent workspace: a home for your AI development agent

A persistent server workspace for Codex, Claude, developer tools, tests, browsers and repositories.

Workspace for AI agents

AI agent workspace: a home for your AI development agent

An AI agent works best when it has a persistent place: repositories, dependencies, a test browser, artefacts, logs, secrets, tool access and backup. That can be a cloud server, VPS, Cloud Pro or dedicated server in DataHouse.

Short answer

An AI agent works best when it has a persistent place: repositories, dependencies, a test browser, artefacts, logs, secrets, tool access and backup. That can be a cloud server, VPS, Cloud Pro or dedicated server in DataHouse.

Persistent work environment

The workspace stores code, dependencies, cache, test results, screenshots, reports and tools used by the agent between tasks.

Codex, Claude and build tools

The server can host CLI tools, runtimes, containers, Playwright, browsers, SDKs, compilers, log analyzers and company scripts.

Access and secrets control

An AI workspace needs SSH/VPN/firewall rules, user separation, secure API-key storage and a clear policy for what the agent may run.

From CPU to GPU

Most development work runs on CPU. Add GPU only when the workspace performs inference, model tests or data processing.

Practical checklist

  1. Choose the scope: repositories, builds, UI tests, crawl, documentation, logs and automations.
  2. Size the server, storage, backup, operating system, containers and tools required by the agent.
  3. Define access: SSH, VPN, firewall, users, API keys, secrets and command audit.
  4. Add monitoring, resource limits, snapshots and a restore procedure after agent errors.
  5. If the agent runs inference, consider GPUaaS, a Blackwell-class server or a separate model endpoint.

Frequently asked questions

Does an AI workspace need GPU?

No. For Codex, Claude, builds and UI tests a strong VPS, Cloud Pro or CPU dedicated server is often enough.

Can an agent work on production?

Not without strict control. A workspace with copies, staging, repository access and limited permissions is safer.

Can DataHouse operate such a workspace?

Yes. Server, updates, backup, monitoring, access and tool separation can be planned.