Reality Drift Tools: Evaluation, Diagnostics, and Governance for AI Systems

Abstract

This collection presents three practical tools developed as part of the Semantic Fidelity Project for evaluating Reality Drift in artificial intelligence systems, including large language models, retrieval systems, and multi-step agent workflows.Reality Drift occurs when a system continues to operate coherently while gradually losing corrective contact with user intent, source conditions, operational context, or real-world outcomes. In production AI systems, this can occur without a conspicuous failure or major decline in benchmark performance. Outputs may remain fluent, consistent, and procedurally valid while becoming less faithful, useful, or responsive to the conditions they are meant to represent.The collection includes:Reality Drift Evaluation Framework — a structured method for identifying representational misalignment, weakened feedback, proxy substitution, and declining semantic fidelity.Reality Drift Diagnostic for Silent Drift in LLM Workflows — a focused diagnostic for detecting drift hidden behind fluent outputs and apparently normal system performance.Reality Drift Governance Readiness Audit — an organizational audit of the monitoring, verification, escalation, and corrective mechanisms needed to identify and respond to Reality Drift.Together, these tools extend conventional model-drift monitoring beyond changes in data distributions and measurable performance. They examine whether an AI system remains meaningfully connected to its purpose, evidence, users, and operating environment.The collection forms the applied evaluation and governance component of the Semantic Fidelity Project, an independent research initiative examining how meaning is preserved, distorted, and degraded across artificial intelligence systems and repeated layers of representation.The tools are intended for AI engineers, governance and risk teams, organizations deploying language models or agent-based systems, evaluators, auditors, and researchers working on AI reliability and alignment.