Override Pattern Analyzer

Software componentHuman OversightExperience & Human Oversightarc:InterventionPatternAnalyzer

An analysis component that aggregates human overrides of agent decisions by case category to surface systematic agent misclassification patterns for recalibration.

Responsibility. Detects systematic agent errors from patterns in human overrides.

Also known as: Mutual performance monitoring (human-to-AI), Approval decision-boundary analysis, Override pattern analysis, Monitoring-to-improvement feedback loop, Override rate tracking, Adherence tracking, Override Pattern Analyzer

Direct neighbourhood (hover for relationship types)

Relationships

reads dependency

sends data to dynamic

Design guidance

SHOULD surface override patterns rather than silently correcting each individual error.
SHOULD feed approval and override outcomes, with full decision context, back into confidence calibration and model refinement.
SHOULD pair quantitative override rates with structured qualitative reasons to locate specific deficiencies.
High override rates in specific categories SHOULD trigger automatic model review and potential retraining.
Systematic divergence MAY indicate policy clarification needs or warrant reduced agent confidence for that category.

Quantitative guidance

As stated by the sources; verify before use.

Example: 12 of 15 high-risk flags on self-employment applications overridden in one week (Ch10.2).
Illustration: humans approving 85% of claims under $10k but 40% of $10k-$50k claims should reduce requests for low-value claims and increase information gathering for medium-value claims (Ch10.4).

Classification

Patterns: Mutual performance monitoringData flywheelFive-phase monitoring-to-improvement loop (detect, root-cause, correct, staged rollout, verify)Human-agent divergence analysis
Technologies: Cleanlab
Quality attributes: Maintainability (ISO/IEC 25010)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
Risks mitigated: Silent individual corrections that leave systematic errors in placeStatic agent performance despite accumulated approvalsUnnecessary approval requests for clear-cut casesAgent miscalibrationKnowledge gapsUnclear policies

Sources

Ch10.2: T. Nguyen, "Proactive Agents," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.2. ISBN: 9798244538229.
Ch10.4: T. Nguyen, "Human-in-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.4. ISBN: 9798244538229.
Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.
Ref10.01: "Human-in-the-Loop Systems for Agent Interactions," unpublished reference note (01-Human-in-the-Loop-Systems.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note