Precondition Checks, Not Prompts, Can Stop AI Agents From Acting Prematurely

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A developer at DOS AI has outlined a mechanism to prevent AI agents from executing high-stakes tool calls based solely on language model judgment. The approach attaches structured preconditions directly to functions, which are verified by the tool executor before any call is allowed to proceed. Conditions can require that a customer has sent a document, that a specific lead field is filled, or that another function was previously called. When a check fails, the model receives an explicit error message rather than a silent refusal, prompting it to request missing information and retry. The author emphasizes this is not a replacement for authorization or rate limiting, but a targeted safeguard against model hallucination in consequential workflows.

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