Seven rules to make AI agents reliable beyond the demo stage

AI agents that work flawlessly in demos often fail in real-world use by looping, hallucinating tool calls, or ignoring output formats — and the root cause is almost always poor specification, not the underlying model. A developer who has shipped multiple agents outlines seven practical rules to improve reliability, none of which require switching to a larger model. Key fixes include writing falsifiable output instructions, giving each tool a single unambiguous purpose with plain-language error messages, and enforcing hard limits on steps and runtime in code rather than in prompts. Dangerous or irreversible actions should be gated programmatically, not just requested in natural language, since prompt instructions are requests the model usually but not always follows. For Claude Code users specifically, the author provides three copy-paste hook configurations that block writes to sensitive paths, prevent destructive shell commands, and auto-format files after every edit.