Agentic AI Reference Architecture

Model Adaptation · Data artifact

Synthetic Dataset

Data artifactModel AdaptationModelsarc:SyntheticDataset

A generated dataset of diverse, realistic scenarios (e.g., AML, card fraud, bot attacks) for training and evaluation.

Responsibility. Supplies synthetic scenarios for training and evaluation.

Also known as: Synthetic fraud dataset, Synthetic benchmark, Synthetic legal benchmark, Personal-data-free synthetic training data

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Direct neighbourhood (hover for relationship types)

Relationships

is read by dependency

sends data to dynamic

is produced by lifecycle

Design guidance

Classification

Quality attributes
Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)

Sources

  1. 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.
  2. 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.
  3. Ch7.5: T. Nguyen, "NeMo Curator, Riva Speech AI & Multimodal Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.5. ISBN: 9798244538229.
  4. Ch9.7: T. Nguyen, "GDPR and Data Protection Regulations," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.7. ISBN: 9798244538229.
  5. Ref1.05: Q. Wang, W. T. Tsai, T. Shi, Z. Liu, and B. Du, "Catch me if you can: A multi-agent synthetic fraud detection framework for complex networks," in Proc. IEEE 41st Int. Conf. Data Eng. (ICDE), 2025, pp. 3629-3641, doi: 10.1109/ICDE65448.2025.00271.