The Semantic Field Coherence Score (SFCS): An Observability Metric for Early Detection of Semantic Field Degradation and Domain Drift in AI Systems

Operational AI assurance requires mechanisms capable of detecting semantic instability before it manifests as observable failures. Existing monitoring approaches primarily focus on accuracy, safety, factuality, benchmark performance, and hallucination detection, providing limited visibility into whether an AI system remains anchored within its intended semantic field. This paper introduces the Semantic Field Coherence Score (SFCS), an observability metric designed to quantify semantic field coherence across repeated AI outputs. Rather than measuring truth, correctness, or logical agreement, SFCS measures the degree to which outputs remain anchored within a shared semantic field. To evaluate the metric, we developed the SFCS Validation Workbench and conducted controlled divergence experiments, contradiction analysis, domain-shift testing, and cross domain drift validation spanning finance, artificial intelligence, medical, and legal domains. Experimental results demonstrated systematic degradation of SFCS under progressive semantic divergence and domain drift conditions while contradictory statements within the same semantic field maintained high coherence scores. Cross-domain experiments revealed comparable degradation trajectories across multiple domains. These findings suggest that semantic field degradation may be observable before complete semantic collapse occurs, positioning SFCS as a potential early-warning signal for emerging domain drift and instability in operational AI systems.