Give every AI agent a nano computer.
Sandock gives coding agents files, terminal, browser previews, POSIX volumes, and lifecycle APIs so they can keep state, resume work, and evolve across tasks.
Give Claude Code, Codex, or your custom agent a cloud computer that keeps project state.
Disposable runtimes make agents forget their work.
Dependencies and generated files vanish between runs.
Browser state, preview URLs, and terminal logs are detached from the agent loop.
Long-running coding work restarts instead of compounding.
Build a workflow around Persistent Cloud Sandbox for AI Agents.
A computer-shaped sandbox
Files, terminal, browser, preview, volumes, lifecycle, and API in one agent runtime.
Persistent enough to evolve
Agents can pause, resume, inspect prior work, and build on project context instead of starting over.
Built for coding-agent products
Run Claude Code, Codex, MCP servers, browser agents, and custom agent workflows without building infra first.
The practical building blocks behind Persistent Cloud Sandbox for AI Agents.
POSIX persistent volumes
Keep repos, dependencies, caches, generated files, and task artifacts across agent runs.
Terminal and file APIs
Let agents run commands, inspect output, write files, and continue work through API calls.
Browser previews
Support web app debugging loops with preview URLs and browser-oriented workflows.
Lifecycle control
Create, start, pause, resume, and delete sandboxes as part of agent orchestration.
Start with the workflow that hurts today.
Pick one workflow with repeated demand, visible ownership, and measurable output. Buda gives that team agents, memory, tools, and a shared workspace.
AI coding agents
Give Claude Code, Codex, or your custom agent a cloud computer that keeps project state.
MCP server hosting
Run MCP tools in isolated environments with controlled files, commands, and lifecycle.
Browser automation
Let agents run app previews, inspect behavior, and debug web workflows in the sandbox.
Start with one useful workflow, then expand.
Choose one workflow
Start with AI coding agents. Keep the scope narrow enough that the output is easy to review.
Assign agents and controls
Create the workspace, invite members, assign agents, define files, tools, and usage boundaries.
Measure output and spend
Review artifacts, credit usage, ownership, and execution history before expanding scope.
Expand or stop cleanly
If the workflow proves value, repeat the model in another team. If not, you still have a governed record.
Give your agent a computer it can return to.
Start with a persistent cloud sandbox and build toward long-running agent workflows.
Try Sandock