Reality Drift Tools for AI Systems
Three practical tools developed as part of the Semantic Fidelity Project for evaluating drift, diagnosing hidden failures, and assessing governance readiness in AI systems.
AI systems can remain fluent, operational, and apparently successful while gradually losing corrective contact with user intent, source conditions, and real-world outcomes. These tools examine that problem through system evaluation, workflow diagnosis, and organizational governance.
Reality Drift Evaluation Framework for AI Systems (PDF)
A structured framework for evaluating whether an AI system remains aligned with its purpose, evidence, users, and operating environment as representations, workflows, and outputs change over time.
Reality Drift Diagnostic for Silent Drift in LLM Workflows (PDF)
A practical diagnostic for detecting hidden drift in language-model workflows that continue producing fluent and coherent outputs while becoming less responsive to user intent, source conditions, or real-world constraints.
Reality Drift Governance Readiness Audit for AI Systems (PDF)
An organizational audit for evaluating whether the monitoring, verification, escalation, and corrective mechanisms needed to detect and respond to Reality Drift are present and operational.