Your agent needs a sandbox, not a desert

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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.

Related YouTube Video

MCP is all you need — Samuel Colvin, Pydantic (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

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