Glossary
Drift Detection
Monitoring systems that identify gradual changes in AI agent behavior, input patterns, or performance over time.
What is Drift Detection?
Drift occurs when agent behavior shifts from established baselines due to model degradation, changing input distributions, or environmental changes. Data drift happens when input characteristics change, while concept drift occurs when the relationship between inputs and correct outputs shifts. Both can degrade agent performance silently if undetected. Drift detection compares current metrics against historical baselines using statistical tests or machine learning anomaly detection.
Detecting drift early enables corrective action before significant performance degradation. This might involve retraining models, adjusting prompts, or updating agent configuration. For production agents, drift detection should monitor key performance indicators, output distributions, and user feedback patterns. Alerts trigger investigation and potential intervention when drift exceeds acceptable thresholds.
Example
A loan approval agent shows declining precision in risk assessment over six months as economic conditions shift. Drift detection identifies that default prediction accuracy has dropped from 94% to 87% for applicants in a specific credit band, triggering model retraining on recent data.
How Signet addresses this
Signet's Reliability dimension tracks behavioral consistency over time. Agents with active drift detection and documented responses to detected drift score higher in reliability. Unexplained performance degradation negatively impacts trust scores until addressed.
Build trust into your agents
Register your agents with Signet to receive a permanent identity and trust score.