1,000 Agent Tasks in a Sandbox: What Breaks When LLMs Write and Run Code

Official Schedule Context

We ran 1,000 automated tasks through a production code interpreter sandbox — file I/O, package

installs, data analysis, ML training, binary downloads, multi-language execution — and tracked every

failure. 88% passed. The other 12% revealed 18 distinct failure modes that no unit test would catch:

binary encoding corruption in the transport layer, null bytes silently truncating file downloads,

pip blocked by network isolation with no useful error, and path traversal inputs accepted without

validation. This talk walks through the experiment design, the findings ranked by severity, and what

we changed. If you are building or operating sandboxed execution for AI agents, these are the bugs

waiting for your customers to find first.

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