Implementing Statistical Guardrails for Non-Deterministic Agents

Statistical guardrails are programmatic safety layers that sit between non-deterministic AI agents and end users, catching unsafe or unreliable outputs before they reach users. Two concrete approaches are covered: semantic drift detection using cosine distance z-scores to flag off-topic or hallucinated responses, and confidence thresholding using Shannon entropy on log-probabilities to detect when a model is uncertain or likely fabricating facts. A Python implementation of both methods using sentence transformers is provided, demonstrating how traditional statistical measures can make probabilistic AI systems more trustworthy.