Software operations increasingly rely on SLOs, traces, deployment specifications, and change events, yet dashboards and thresholding practices often expose share-like operational signals as separate scalar panels or baseline distances. This can create false alarms under benign redistribution and miss movement toward policy boundaries. Rasmussen's dynamic safety model motivates drift under competing pressures, but operationalizing it for software is difficult because relevant state variables (remaining margin, engineering effort, and risk/impact) are often compositional and their parts evolve. We formulate an automated, artifact-derived drift-monitor design that maps changing software artifacts into a stable compositional monitoring state: it extracts a current part inventory and policy constraints, maps telemetry to a positive composition, stabilizes splits, merges, and renames through lineage-aware canonical groups, and analyzes boundary-directed drift in log-ratio coordinates. The proposed monitor would report drift direction, step-to-boundary, balance-level attribution, and model-health indicators under architectural churn. We specify the approach, identify its zero/noise/lineage assumptions, and report a reproducible synthetic sanity check of boundary-aware drift and controlled part churn.

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