How to Safely Execute AI-Generated Python Code in Agent Workflows (No Docker Required)

Pydantic Monty is a minimal, Rust-written Python bytecode VM that executes AI-generated code in 0.004ms with zero filesystem or network access by default — the missing infrastructure piece for production-grade AI agent workflows. If you've tried to build a real AI agent that writes and runs its own Python code, you've hit the same wall: you can't hand arbitrary code to CPython. Docker solves the isolation problem but adds 195ms of startup latency per execution. For an agent making dozens of code-execution decisions per task, that compounds to seconds of overhead per workflow. Sandbox cloud services (Modal, E2B) cut it to ~1000ms. Still too slow. Monty cuts it to 0.004ms because it runs inside your existing process — no container spawn, no network call. Samuel Colvin, the creator of Pydantic, built it and released v0.0.1 on January 27, 2026. It hit 2,600 GitHub stars within 48 hours. As of v0.0.8 (March 10, 2026), it is experimental but already usable for constrained code-execution use