Observability & Evaluation · Software component
A/B Test Traffic Splitter
Software componentObservability & EvaluationObservability & Evaluationarc:ABTestRouter
A routing component that assigns live users to control or treatment agent variants according to configured allocation percentages.
Responsibility. Routes production traffic cohorts between baseline and candidate agent versions.
Also known as: Traffic allocator, Experiment assignment, A/B testing, A/B Test Manager, Online Experiment Controller, A/B Test Traffic Splitter, A/B testing of served LLM versions
Variant of Agent Version Experimenter abstract
When to choose. Choose when comparing versions on live outcome metrics with users actually served by the new version, controlling for temporal and population effects.
Relationships
is configured by structural
invokes dependency
is invoked by dependency
emits telemetry to dynamic
routes to dynamic
triggers dynamic
- Rollout Manager abstract Ch3.4 Ref10.05
evaluates assurance
- Agent Controller abstract Ch3.4
alternative to variability
Design guidance
- SHOULD reserve online A/B tests for major model or logic changes that passed offline filtering; small tweaks SHOULD rely on offline evaluation.
- SHOULD expose a tuned configuration to a limited traffic share in parallel with the baseline before full rollout.
- MUST roll back and investigate when production metrics contradict offline evaluation.
- SHOULD compare variants on success rate (primary), cost per request, user satisfaction, latency and hallucination rate.
- SHOULD validate each improvement with a small-scale experiment compared to baseline before deployment.
Quantitative guidance
As stated by the sources; verify before use.
- Typical start: 10% treatment / 90% control; 10-20% of traffic for validation (Ch3.1A).
- Healthcare feedback example: 20% of traffic to enhanced version; satisfaction 65% -> 79%, reformulation 38% -> 22%, abandonment 25% -> 12%, escalation 18% -> 8% (p < 0.001) (Ch3.2).
- Candidate configuration deployed to 10-20% of production traffic before gradual rollout to 100% (Ch3.4).
- Minimum 100-200 samples per variant, longer for small effect sizes; example 50/50 split (Ref8.03).
- Small-scale validation experiment on 5% of traffic (Ref10.05).
Classification
- Patterns
- A/B testingOnline evaluationRandomized controlled comparisonGradual rolloutRollback on contradicting metricsA/B testing of data-quality improvements
- Quality attributes
- Safety (ISO/IEC 25010 | NIST AI RMF: safe)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
- Risks mitigated
- Confounding by temporal and population differencesOffline evaluation not predicting production performanceFailure modes that appear only at scale
Sources
- Ch3.1A: T. Nguyen, "Implement Evaluation Pipelines and Task Benchmarks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.1A. ISBN: 9798244538229.
- Ch3.2: T. Nguyen, "Compare Agent Performance Across Tasks and Datasets," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.2. ISBN: 9798244538229.
- Ch3.3: T. Nguyen, "Web Navigation and Interaction Benchmarks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.3. ISBN: 9798244538229.
- Ch3.4: T. Nguyen, "Tuning Model Parameters for Production Performance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.4. ISBN: 9798244538229.
- Ch5.11: T. Nguyen, "Rule-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.11. ISBN: 9798244538229.
- Ch6.4: T. Nguyen, "Data Quality Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.4. 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.
- Ch10.1: T. Nguyen, "Conversational UI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.1. ISBN: 9798244538229.
- Ref4.03: M. Zhang, J. Wyman, I. M. Bhosale, and W. Tan, "Scaling LLMs with NVIDIA Triton and NVIDIA TensorRT-LLM using Kubernetes," NVIDIA Technical Blog, Oct. 22, 2024. [Online]. Available: https://developer.nvidia.com/blog/scaling-llms-with-nvidia-triton-and-nvidia-tensorrt-llm-using-kubernetes/
- Ref8.01: LangChain, "LangSmith observability: AI agent observability platform," LangChain. Accessed: Sep. 27, 2026. [Online]. Available: https://www.langchain.com/langsmith/observability
- Ref8.03: "Agent Evaluation Frameworks and Metrics," unpublished reference note (03-Agent-Evaluation-Frameworks.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref10.05: "The Data Flywheel: Continuous Improvement Loop," unpublished reference note (05-Data-Flywheel-Continuous-Improvement.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note