How to Set Up Agent Drift Detection and Alerting | Inference Systems

DRIFT TYPES

Concept Drift vs. Data Drift: Detection Methods

A comparison of two primary drift types in agentic systems, detailing their definitions, detection techniques, and monitoring KPIs.

Feature	Concept Drift	Data Drift
Core Definition	Change in the statistical properties of the target variable the model is trying to predict.	Change in the statistical properties of the input data the model receives.
Agentic Manifestation	Agent's success rate or decision quality degrades despite receiving valid inputs.	Agent receives unfamiliar or anomalous input data, causing unexpected behavior.
Primary Detection Method	Monitor agent performance KPIs like task success rate, cost per successful task, or human correction frequency.	Monitor input data distributions using statistical tests on feature values.
Key Statistical Tests	Performance monitoring, PSI on prediction outputs, custom business logic evaluators.	Population Stability Index (PSI), Kolmogorov-Smirnov test, multivariate drift detectors.
Alerting Threshold Example	Task success rate drops by >5% over 24 hours.	PSI score > 0.2 for any critical input feature.
Common Mitigation	Trigger retraining of the agent's reasoning model or LLM using a continuous learning loop.	Update data preprocessing, implement data quality checks, or expand the agent's context window.
Monitoring Tools	Grafana dashboards, Datadog custom metrics, Weights & Biases for experiment tracking.	Evidently AI, Arize AI, Great Expectations for data validation.
Link to Related Guide	See our guide on How to Design a Continuous Learning Loop for AI Agents.	See our guide on Launching a Governance Model for Autonomous Agent Deployments.