Code generation language models (LLMs) offer significant benefits, such as reducing cognitive load and accelerating iteration by serving as effective pair programmers for well-defined tasks, but they also pose risks, including using undeclared variables or outdated packages. To mitigate these risks, sandboxed execution environments have become essential, allowing LLMs to test and iterate on code within a controlled space, thereby preventing potential harm to production code or infrastructure. Sandboxes are particularly beneficial for coding agents, enabling them to write, refactor, and execute code autonomously without affecting the local environment. This isolation is crucial for multi-tenant systems, ensuring security and stability by preventing cross-tenant contamination. Koyeb Sandboxes provide a robust platform for running AI-generated code safely, offering features like isolated environments, autoscaling, and serverless infrastructure, making high-velocity experimentation possible without compromising security or infrastructure integrity.