Observability & Evaluation · Software component
Comparative Trace Analyzer
Software componentObservability & EvaluationObservability & Evaluationarc:ComparativeTraceAnalyzer
An analysis component that aligns successful and failed traces, or trace distributions from repeated identical runs, to locate divergence points and conditions statistically correlated with failure.
Responsibility. Identifies where and under what conditions failed executions diverge from successful ones.
Also known as: Comparative inspection, Path-level inspection, Statistical trace analysis, Root-cause analysis: affected-trace comparison
Relationships
reads dependency
evaluates assurance
Design guidance
- SHOULD run the same task multiple times with identical inputs and analyze failure patterns across the trace distribution rather than a single trace.
- SHOULD promote reasoning patterns found only in successful traces and suppress those found only in failed traces.
Quantitative guidance
As stated by the sources; verify before use.
- Example: success rates of 95% vs 60% indicate different failure characteristics (Ch3.6).
- An agent may fail 40% of the time on the same question despite a successful sample trace (Ch3.6).
Classification
- Patterns
- Repeated-execution trace distributionsContrastive success/failure analysisReasoning stability measurement
- Quality attributes
- Maintainability (ISO/IEC 25010)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
- Risks mitigated
- False conclusions from a single non-deterministic traceBrittle reasoning paths
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.
- Ref8.04: "Data Quality and Drift Detection for Agent Systems," unpublished reference note (04-Data-Quality-Drift-Detection.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note