Behavioral Drift
Gradual, often undetected changes in an AI agent's decision patterns or outputs over time.
Full Definition
Behavioral drift occurs when an AI agent's outputs, decision patterns, or performance gradually change over time without explicit model updates. This can happen due to changes in input data distributions, evolving user interactions, degradation in external API responses, or subtle shifts in how the agent interprets its instructions. Drift is particularly dangerous because it often happens slowly enough to escape manual review, and by the time it's noticed, significant damage may have already occurred. Continuous monitoring systems detect drift by tracking statistical distributions of agent behaviors, comparing current patterns against established baselines, and alerting operators when deviations exceed configured thresholds.
Related Terms
Anomaly Detection
The automated identification of unusual patterns or behaviors in AI agent operations that deviate from expected norms.
AI Governance
The framework of policies, processes, and technologies used to ensure AI systems operate ethically, transparently, and in compliance with regulations.
Model Monitoring
Continuous tracking of AI model performance, behavior, and output quality in production environments.