Agent Sandbox - Kubernetes-Native Sandboxes for AI Agent Code Execution

Fri, 21 Aug 2026 00:00:00 +0000

A few months ago I built a Kubernetes agent with Google’s ADK for Go. That agent talked to the cluster: it ran kubectl against my kind cluster and explained what it saw. This post is the inverse problem, and it is the one that actually keeps me up at night: when an LLM writes code, where does that code run?

The honest answer for most agent stacks today is “on whatever machine the agent process happens to be on.” The LLM generates a shell command or a Python script, the framework execs it, and everyone hopes the model never emits rm -rf or something that exfiltrates the credentials sitting in the environment. Hosted sandboxes like E2B and Modal exist precisely because of this, but if you already operate Kubernetes, shipping your agents’ code execution to a third-party SaaS feels backwards. You have a scheduler, you have network policy, you have runtimes with kernel isolation. What you were missing was the workload abstraction.

Agent-Sandbox on SREKubeCraft | Nick Nikolakakis

Agent Sandbox - Kubernetes-Native Sandboxes for AI Agent Code Execution