AI agents keep failing. The fix is 40 years old. — Cyrus Radfar

AI agents fail in production because they're deployed into codebases with hidden dependencies, mutable global state, and undeclared side effects that make their outputs non-deterministic and unverifiable. The problem isn't model capability but architectural design—agents can only work with explicit function contracts and have no way to discover implicit context that human developers accumulate over months. Applying functional programming principles (formalized as the SUPER framework and SPIRALS process) eliminates hidden state and makes code deterministic enough for agents to reason about and debug reliably.

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