Your prompt system has no tests, and that is why you cannot tell it is broken

In a recent article on Dev.to, developer Latifox argues that AI-driven agent systems often fail silently because they produce plausible-looking but incorrect outputs. To address this, the author proposes a rigorous testing framework for prompt-based systems. Instead of evaluating prose, the approach focuses on three deterministic pillars: recomputing arithmetic, validating JSON schema structures, and enforcing numeric constraints on text output. By implementing a lightweight, dependency-free Python checker that runs on every file write, developers can catch regressions early. The author emphasizes the importance of 'testing the tests' using fixtures and mutation testing to ensure the validation logic itself is sound. By integrating this checker into agent workflows, systems can self-correct errors in real-time. The article concludes that structured validation is essential for moving beyond 'confident nonsense' in LLM-based applications, providing a practical blueprint for building more reliable AI agents.