Secure Cloud Sandboxes for Autonomous Agents
The Incord Team·June 2026·7 min read
Giving an agent the ability to execute code and use a browser is powerful, but doing it safely is hard. Discover how Incord's ephemeral Cloud Agents provide isolated compute environments.
Execution Without Risk
When an autonomous agent decides to run a Python script, execute a bash command, or navigate a live web page, it needs a place to do it. Executing that action locally on your own infrastructure or in a shared container exposes you to massive security risks. A hallucinated `rm -rf` command or a maliciously injected script from a user prompt can bring your entire backend down.
Incord solves this by spinning up ephemeral, isolated Cloud Sandboxes for every single agent session. The agent has a full Ubuntu OS environment to work in—complete with Node.js, Python, Chrome, and bash access. It can compile code, run data analysis, or execute web automation workflows just like a human developer would.
But the moment the task is complete, or the session ends, the entire sandbox is destroyed. Every bit of state, every downloaded file, and every executed script vanishes. It is the ultimate zero-trust execution environment.
Serverless Agent Compute
Maintaining idle infrastructure to support unpredictable agent execution is incredibly wasteful. If you have 1,000 users, but only 5 are running complex data analysis at any given moment, keeping 1,000 containers warm will bankrupt your project.
With Incord, you pay per GB-second of compute only while the agent is actively processing a task. When the sandbox spins up, the billing starts. When it's destroyed milliseconds or minutes later, the billing stops.
This serverless approach to agent compute means you can scale from zero to tens of thousands of concurrent agents instantly, without ever worrying about capacity planning, idle server costs, or infrastructure maintenance.
Native Platform Integration
Because the sandboxes are part of the Incord Unified Intelligence Layer, they natively understand how to talk to your agents. You don't need to write complex API wrappers to get the results of a Python script back into the LLM's context window.
The agent writes the code, the sandbox executes it, and the stdout/stderr streams are piped directly back into the agent's context as tool responses. It is a seamless loop of reasoning and execution.