Observability & Evaluation · Data artifact

Interpreted Feature Library

Data artifactObservability & EvaluationObservability & Evaluationarc:InterpretedFeatureLibrary

A curated mapping from discovered sparse features to the concepts or reasoning patterns (correct or erroneous) they represent, built by systematic analysis of activations.

Responsibility. Documents what each monitored feature means.

Direct neighbourhood (hover for relationship types)

Relationships

configures structural

Feature Activation Monitor Ch3.6

Design guidance

SHOULD be built through systematic analysis of when and how each feature activates before relying on feature monitoring.

Classification

Quality attributes: Explainability (NIST AI RMF: explainable and interpretable)
Risks mitigated: Misinterpreted feature signals

Sources

Ch3.6: T. Nguyen, "Trace Analysis and Execution Debugging," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.6. ISBN: 9798244538229.