Monitoring

Monitoring is the continuous practice of watching how a model behaves after it’s deployed — tracking metrics, reviewing samples, and alerting when something looks off. Unlike one-time evaluations, monitoring captures change over time: it can detect when a model update introduced a regression, when user behavior shifted in ways that expose new failure modes, or when a behavioral metric has been drifting in an undesirable direction. Good monitoring setups combine automated signals (metric dashboards, anomaly detection) with periodic human review. For behavior architects, monitoring is what closes the loop between deploying a model and knowing whether it continues to behave as intended — because “it worked in testing” is never a guarantee that it will keep working in production.