The Sandboxed Code Execution Tool
Execute code an agent writes inside an isolated, resource-limited sandbox and get back stdout, errors, and artifacts — so agents can compute, test ideas, and transform data without touching your real infrastructure.
A suggestion isn’t a shipped change
An agent that can only propose code still leaves all the work to a human. To actually deliver, it needs a real, governed workspace where it can run code, edit files, test, and use Git — safely, and without touching anything you didn’t allow.
Read-only agents
Suggestions still require a human to run, test, and commit everything.
Unsafe execution
Running agent-generated code on real infrastructure is a security risk.
No verification
Without tests and builds, an agent can’t know its change works.
Ungoverned Git
Direct repo access with no policy or audit is a non-starter in the enterprise.
Sandboxed Code Execution, without the risk
Capability
What it does
Run agent-written code safely in an isolated sandbox.
it runs a snippet of code in an isolated, resource-limited sandbox and returns the output, errors, and any artifacts.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
each run is ephemeral, network-restricted, and time- and memory-bounded inside your perimeter, so agent-generated code executes without risk to production systems.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
Execution runs in an isolated, on-premise sandbox scoped per tenant with full command and file audit logging, so an agent can do real work on your code without unsafe access or anything leaving your perimeter.
Per-tenant, logged
Parameters
The code_execute_sandbox tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: python Optional Runtime language for the code. pythonnodebash
default: 30 Optional Maximum execution time.
How the Sandboxed Code Execution tool works in practice
Sandboxed Code Execution is a code execution & workspace tool you assign to a VDF AI agent. It runs a snippet of code in an isolated, resource-limited sandbox and returns the output, errors, and any artifacts. Its hallmarks — Sandboxed, Isolated, Bounded — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, each run is ephemeral, network-restricted, and time- and memory-bounded inside your perimeter, so agent-generated code executes without risk to production systems. It expects code as required input, so calls are explicit and easy to audit. Every call is scoped to the requesting tenant and written to an audit log, so the capability is safe to run inside a regulated, on-premise environment — the same governance model behind every VDF AI tool.
Teams reach for Sandboxed Code Execution when they need to handle data crunching, code validation, and transformations. It rarely works alone — pair it with Terminal Execute, Test Runner, and File Write to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Sandboxed Code Execution pays back
Data crunching
Let an agent compute a result it can’t reason out in tokens.
Code validation
Actually run a snippet to confirm it works before proposing it.
Transformations
Reshape data on the fly inside a task.
Tool prototyping
Try logic in a safe space before promoting it to a real tool.
Assigned to agents, orchestrated as networks
On VDF AI, an industry’s use cases map to agents, and you assign tools like this one to those agents. Compose multiple agents into a governed, on-premise network.
What changes after you assign it
Questions about the Sandboxed Code Execution tool
What is the Sandboxed Code Execution tool?
It runs a snippet of code in an isolated, resource-limited sandbox and returns the output, errors, and any artifacts. Assigned to a VDF AI agent, it runs under role-based policy with full audit logging so the capability is safe to use in production.
Is it safe to run agent-generated code?
Yes. Execution is isolated, network-restricted, and resource-bounded inside your perimeter, so code can’t reach production systems or exfiltrate data.
What languages are supported?
Common runtimes such as Python, Node, and shell, selectable per call.
What inputs does the Sandboxed Code Execution tool need?
It requires code, and optionally accepts language, timeout_seconds, and stdin. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Sandboxed Code Execution?
Sandboxed Code Execution is commonly assigned alongside Terminal Execute, Test Runner, and File Write. On VDF AI you compose several tools and agents into a single governed, on-premise network.
Does it run on-premise?
Yes. Like every VDF AI tool, it can run on-premise or in your sovereign cloud, scoped per user and audit-logged, so your data never leaves your perimeter.
How do agents use it?
You assign the tool to an agent under a role-based policy; the agent calls it as one step in a task, and several agents and tools can be orchestrated together as a governed VDF AI Network.
Assign Sandboxed Code Execution to these agents
These VDF AI agents can be assigned this tool. Open an agent to see the full toolkit it can run.
Tools that work well alongside this one
Where this tool delivers value
Put Sandboxed Code Execution to work
See the Sandboxed Code Execution tool assigned to an agent and orchestrated in a governed, on-premise network.