pyursa: A DataFrame Library Designed to Fail Loudly for AI Agents

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The author argues that AI agents writing data code need libraries that expose mistakes rather than hide them, since agents often lack a human reviewer to catch silent errors. Pandas is cited as an example that can return wrong results without warning, such as an integer sum overflowing into a negative number. The new library, pyursa, responds with stable error codes, machine-readable JSON error output, and fix suggestions that an agent can apply directly. Errors are raised when the query plan is built, so mistakes surface before any data is processed. The library also enforces strict typing rules, including exact decimal arithmetic, rejecting lossy casts, and treating time zones as part of the type.

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