dailyai.report

Every monitoring dashboard reads “healthy” while decisions slowly drift. Engineers expect crashes or sensor failures to flag trouble, but quiet drift slips through in AI systems. The pattern emerges as autonomy spreads, turning subtle bias into real‑world errors. Practitioners must develop monitoring that captures drift, not just status. Without such tools, systems risk eroding trust before any crash occurs.