Reality Anchoring: An External, Priority-Driven Enforcement Layer for Mitigating Over-Reliance in Human-AI Interaction aka Eliza Effect

Reality Anchoring As large language models and agentic AI systems become integral to safety-critical and compliance-sensitiveworkflows, users increasingly exhibit over-reliance, anthropomorphic misattribution, and delayed oromitted human judgment—a phenomenon known as the Eliza effect. We introduce reality anchoring, an external, model-agnostic middleware layer that interrupts ongoing interaction when high-priority tasks are detected and enforces explicit, accessibility-aware acknowledgment before resuming control. The system employs a configurable priority engine, temporal look-ahead scanning, a finite state machinefor enforcement, adaptive authentication paths, graduated escalation, and external output validation. Ahybrid edge-cloud architecture ensures low-latency local interruption and enterprise-grade auditability.We present the conceptual framework, system architecture, enforcement logic with figures, user storieswith tasks and acceptance criteria, minimum viable product scope, performance analysis, and a buy vsbuild discussion for regulatory compliance.