AI Engineer World's Fair 2026 — unofficial import demo

Everyone agrees agents need code execution. That agreement lasts right up until you ask how to do it. The default answer is usually something like "My agent needs a full Linux VM to succeed". That's a very convenient answer for sandbox providers, but I think it's often incorrect. In many real-world agent workflows, the model does not need a whole computer. It does not need arbitrary packages, shell access, CPython, node, let alone `awk` `sed` and `gcc`. It needs a small amount of safe, expressive compute: enough to write code, call tools, and keep intermediate state out of the context window. That is the idea behind Monty: a minimal Python interpreter, written in Rust, designed specifically for running code written by agents. In this talk, I'll argue that for a surprisingly large class of agent systems, a curated set of tools in a custom runtime is better than a full sandbox. Not because full sandboxes are bad, but because they solve a much larger problem than most embedded agents actually have. And you pay for that mismatch in complexity, cost, operational pain, and 100,000X higher latency. Sandboxes are great, but there's such a thing as too much sand - in many scenarios the constraints and limitations of a custom built, minimal sandbox are a feature, not a bug.