Sandboxing AI-Generated Code: How I Run Autonomous Agent Output Safely
September 1, 2026
How an autonomous coding agent can safely write, run, and test its own code — isolated Docker sandboxes, resource limits, and cleanup, from building a Devin AI clone.
The moment an AI agent can write and execute its own code, you've inherited a new kind of trust problem: you have no idea in advance what that code will do. Building an autonomous coding agent (a Devin-style AI software engineer) meant treating every generated snippet as untrusted by default, no matter how reasonable the plan behind it looked.
Why sandboxing has to come first
It's tempting to prototype an AI agent by just running its output directly in your dev environment — call an LLM, get back a shell command or a file diff, execute it. That works right up until the agent decides to run `rm -rf` on the wrong path, install a package that phones home, or spin up a process that never exits. None of that requires malice; a confidently wrong plan is enough.
So the architecture starts with isolation, not features. Every session gets its own Docker container, created fresh, torn down after the task completes or times out. Nothing about the host filesystem, network, or environment variables is visible inside unless it's explicitly mounted in.
What each sandbox actually gets
Each container is given a working directory, a constrained set of installed tools, and hard resource limits — CPU shares, memory ceilings, and a wall-clock timeout so a runaway loop can't hold a sandbox open indefinitely. Network access is scoped to just what a build step needs (package registries), not the open internet, which also cuts down on an agent 'helpfully' pulling in a dependency you never asked for.
Running 50+ of these concurrently changes the calculus again — you're not just isolating one risky process, you're managing a fleet of them. Container startup time becomes a real cost, so the system keeps a small pool of pre-warmed containers ready to be handed a task rather than building one from a cold image every time.
Feeding results back to the agent
The other half of the problem is getting useful signal back out. Stdout and stderr get captured and streamed to a live dashboard so a human (or the agent's own reasoning loop) can see what actually happened, not just whether the exit code was zero. Test failures, lint errors, and stack traces all get parsed and handed back to the LLM as structured context for the next step, rather than a wall of raw terminal text.
What I'd do differently next time
If I were starting over, I'd invest earlier in a proper container pool with pre-installed dependency caches — image pulls and cold starts were the single biggest source of latency in early versions. I'd also add a dry-run mode that diffs proposed filesystem changes before anything executes, since most of the scary failure modes (deleting the wrong file, overwriting configuration) are things a diff would catch before a sandbox even needs to run.
None of this is unique to AI agents — it's the same instinct you'd apply to running any untrusted user-submitted code. The difference is that an LLM will generate that code faster and with more confidence than any human submitting a support ticket, so the isolation has to be the default, not an afterthought.