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The article shares seven practical code-based guards to make AI agents more reliable by mitigating failures, such as verifying actions before execution and ensuring state consistency, which complement prompt improvements.
The author argues that the reliability of AI agents comes from deterministic code, not the LLM, and shares five key practices for building trustworthy agents on messy real-world data.