Reality Drift Tools for AI Systems
Three practical tools for evaluating drift, diagnosing hidden failure, and assessing whether organizations can detect and correct misalignment in AI-supported systems.
AI systems can remain fluent, useful, and operational while gradually becoming less faithful to user intent, source context, task purpose, or real-world conditions. These tools are designed to locate where that loss occurs, distinguish visible performance from deeper representational failure, and determine whether corrective mechanisms still have authority.
Reality Drift Evaluation Framework for AI Systems (PDF)
A layered method for locating representational misalignment across data, model performance, behavior, Semantic Fidelity, workflow, and correction. It separates where drift becomes visible from the process that produced it, then identifies what must regain authority for the system to reestablish contact with reality.
Reality Drift Diagnostic for Silent Drift in LLM Workflows (PDF)
A practical diagnostic for LLM workflows that continue producing fluent and apparently correct outputs while losing intent, context, constraints, provenance, or grounding. It evaluates the complete workflow rather than assuming the model alone is responsible for drift.
Reality Drift Governance Readiness Audit for AI Systems (PDF)
An organizational audit for determining whether AI governance mechanisms can actually detect and correct drift after deployment. It evaluates orientation, representational integrity, detection, and corrective capacity, with emphasis on whether real-world evidence can interrupt operation and change what happens next.
The three tools move from evaluation, to workflow diagnosis, to organizational governance. The common test is whether internal success can continue while the underlying condition deteriorates, and whether external evidence still has enough authority to force correction. Coherence is not contact.