An agent sandbox is an isolated execution environment where an AI agent can inspect files, run commands, edit artifacts, use tools, and preserve workspace state without directly affecting the host system.
The sandbox is a security and reliability boundary for workspace-centric tasks. It is especially useful for coding agents, document processing, data-room review, generated artifact verification, and multi-agent workflows where each sub-agent needs its own workspace.
An agent sandbox commonly includes:
- a manifest describing initial files, mounts, environment variables, users, and permissions;
- filesystem tools for reading, writing, searching, and patching files;
- shell or code-execution access;
- resource limits for CPU, memory, network, and runtime;
- snapshotting for later inspection or resume;
- approval gates for risky operations; and
- trace metadata connecting sandbox actions to the agent run.
An agent sandbox is not just a "tool." It changes the execution boundary. The agent harness prepares the model with sandbox-specific context and binds sandbox-native capabilities to the live workspace.
Isolation strength depends on the implementation. A local directory sandbox, container sandbox, virtual machine, and hosted sandbox have different security properties. Sensitive workloads should use least-privilege credentials, network restrictions, file allowlists, and cleanup policies.
The sandbox also improves product behavior. Long-running work can leave artifacts for human review, and a resumable agent run can continue from saved workspace state instead of rebuilding everything from chat history.
The OpenAI Agents SDK sandbox guide documents sandbox agents, manifests, live sandbox sessions, snapshots, and sandbox run configuration.
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