Observability & Evaluation · Data artifact
Trace Sampling Policy
Data artifactObservability & EvaluationObservability & Evaluationarc:TraceSamplingPolicy
A feature-flag-driven configuration determining per request whether to collect full, sampled, confidence-conditional, user-triggered or minimal traces.
Responsibility. Decides the trace collection level for each request context.
Also known as: Transparency level feature flags, Strategic sampling policy, Selective trace retention policy
Relationships
configures structural
Design guidance
- SHOULD combine triggered sampling (novel queries, low confidence, user flags) with systematic random sampling to ensure distribution coverage.
Quantitative guidance
As stated by the sources; verify before use.
- Persistent sample 10-20% of traces; ephemeral retention of all traces for 24-48 hours; systematic random sampling of 5-10% of inferences (Ch3.9).
- Production systems often trace 1-10% of traffic (Ch6.5).
Classification
- Patterns
- Adaptive transparency
- Quality attributes
- Cost efficiencyTransparency and accountability (NIST AI RMF: accountable and transparent)
- Risks mitigated
- Excess trace overhead at scale
Sources
- Ch1.8: T. Nguyen, "Scalability and Production Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.8. ISBN: 9798244538229.
- 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.
- Ch3.9: T. Nguyen, "Reasoning Quality," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.9. ISBN: 9798244538229.
- Ch6.5: T. Nguyen, "Production RAG Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.5. ISBN: 9798244538229.
- Ch8.2A: T. Nguyen, "Error Rates and Reliability," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.2A. ISBN: 9798244538229.