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Give a Dockerized DeepAgents agent only the network capability its task needs. Prefer a narrowly scoped search or API tool when that is enough; if the agent must run arbitrary code, use an isolated sandbox and deliberately control its outbound network access. Do not treat DeepAgents’ lightweight interpreter as an internet connection, or rely on the model to police itself.
What “internet access” means in DeepAgents
DeepAgents does not grant one universal internet capability. Its tools, execution backend, virtual filesystem, and interpreter are distinct parts of the agent environment. The lightweight JavaScript interpreter is a scoped QuickJS runtime: it does not provide shell access, package installation, filesystem access, or network access. A sandbox backend can add an execute tool for shell commands and code, while other callable tools can provide specific capabilities such as searching or calling an API.
So first identify what the workflow actually needs: reading selected web results, calling a particular service, or making arbitrary outbound requests from executed code. These are different permissions and should not be bundled together by default.
Choose the narrowest execution path that works
| Approach | What it gives the agent | Main security consideration |
|---|---|---|
| Purpose-built search or API tool | A defined operation, such as querying a service, rather than general shell networking. | Limit the tool’s operations, inputs, and destinations to the workflow’s needs. |
| DeepAgents interpreter | A scoped JavaScript runtime for supported computation. | It does not provide network, filesystem, package-installation, or shell access. |
| Local shell backend | Shell commands run with the permissions of the user running them. | Commands may access files, make network connections, execute programs, change system configuration, spawn processes, or install packages. LangChain’s LocalShellBackend guidance recommends an isolated backend for production code execution. |
| Sandbox backend | An isolated environment for shell commands and code; DeepAgents can expose execution through a tool. | A sandbox is an execution boundary, not proof that outbound traffic, credentials, or every host interaction are safe. Verify its actual network policy and isolation for your deployment. |
Set up access in a safer order
- Define the task’s network need. List the operations and destinations the agent must reach. If a purpose-built tool can perform them, use that instead of granting general shell networking.
- Decide whether arbitrary code execution is necessary. If it is not, do not enable shell execution just to obtain internet access. The DeepAgents interpreter itself has no network access.
- Choose an execution boundary. For code execution in production, use an isolated sandbox backend rather than LocalShellBackend. Confirm how the sandbox is separated from the Docker host and what filesystem and process permissions it receives.
- Set outbound policy outside the model. Apply destination allowlists or egress restrictions at the network, host, container, or sandbox layer appropriate to your topology. Verify the controls in the actual deployment; the framework’s sandbox option alone does not establish which destinations are reachable.
- Keep credentials out of untrusted execution. Inspect how environment variables, mounted files, service tokens, and other secrets enter both the Docker container and the selected sandbox. Do not forward application credentials into agent-run code unless the workflow requires them and access is narrowly scoped.
- Test the boundary, not just connectivity. Check that required requests succeed, unapproved destinations are blocked, and agent code cannot access host files or secrets beyond its intended permissions. Repeat the checks after changing the image, backend, network configuration, or credential setup.
Why LocalShellBackend is risky for a network-enabled agent
LocalShellBackend executes commands with the user’s permissions. That makes it a broad trust decision, not simply a switch for downloading packages or visiting a website. LangChain’s reference warns that shell commands can reach files, execute programs, make network connections, modify system configuration, spawn processes, and install packages. It also cautions that virtual filesystem or path restrictions do not provide security when shell access is enabled.
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For a production agent that runs code, use an isolated backend and verify its actual boundary. Putting an application in Docker does not, by itself, establish that its network access is limited to safe destinations or that its execution environment cannot reach sensitive resources. The relevant controls depend on how the container, host, sandbox, and network are connected.
What to verify in a managed sandbox
DeepAgents deployment documentation describes sandbox configuration as optional and names Daytona, Modal, Runloop, and LangSmith Sandbox as options. That list does not establish the current egress rules, credential handling, or isolation guarantees of any provider. Before choosing one, verify the provider’s current documentation and your account’s configuration for:
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- Whether outbound internet access is enabled by default, configurable, or restricted to approved destinations.
- How DNS and outbound connections are controlled, and whether those controls apply to every process the agent can start.
- Which environment variables, secrets, files, or credentials are visible inside the execution environment.
- What the sandbox can access on the Docker host, the application network, and attached storage.
- How the environment is isolated, reset, and cleaned up between jobs or users.
Do not assume that one provider’s settings or security properties apply to another. Check the configuration you will deploy, not only a product overview.
Handle web content as untrusted input
Internet access lets an agent retrieve content that may contain instructions aimed at changing the agent’s behavior. Treat fetched pages and API responses as data, not as authority to grant tools, expose secrets, or initiate consequential actions. Keep tool permissions narrow, and require human approval for consequential operations where your application supports it. As the DeepAgents security guidance puts it: “Enforce boundaries at the tool/sandbox level, not by expecting the model to self-police.”
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There is no safe universal Compose snippet for this setup: the right egress controls depend on your Docker topology and on whether execution happens in the application container, a local sandbox, or a managed sandbox. The relevant controls and labels can also change across Docker Engine and Compose versions. Consult the current Docker documentation for the specific deployment you run, then test the effective policy from inside the agent’s execution environment. Do not infer that a container is safely restricted merely because it is containerized.
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