Observability & Evaluation · Data artifact
Benchmark Suite Manifest
Data artifactObservability & EvaluationObservability & Evaluationarc:BenchmarkSuiteManifest
A declarative selection of benchmarks, datasets and environments with stratification and proportions across task complexity, domain, modality and time, plus composite-score weights, defining a multi-benchmark evaluation.
Responsibility. Declares which benchmarks and strata an evaluation covers and how results are weighted.
Also known as: Benchmark suite, Multi-benchmark evaluation suite, MMLU / HumanEval / GSM8K accuracy suite
Relationships
configures structural
is read by dependency
Design guidance
- SHOULD mirror expected production task distribution rather than academic difficulty.
- SHOULD include domain-specific benchmarks for regulated domains; general-benchmark success is necessary but not sufficient for production readiness.
- SHOULD combine diverse benchmarks, cross-dataset tests, production pilots and user studies rather than relying on any single evaluation source.
Quantitative guidance
As stated by the sources; verify before use.
- Example: agent at 70% overall may be 90% on simple retrieval and 40% on complex reasoning (Ch3.2).
- FinGAIA: 407 financial tasks; WebArena: 812 templated tasks; GAIA: 466 tasks in 3 levels; AstaBench: 11 benchmarks, 2,400+ problems (Ch3.2).
Classification
- Patterns
- Multi-benchmark evaluationComplexity stratification (simple / multi-step / long-horizon)Composite weighted scoring
- Technologies
- AgentBenchWebArenaGAIAtau-BenchAstaBenchFinGAIA
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
- Risks mitigated
- Illusion of readiness from single-benchmark evaluationAggregate scores masking capability gaps
Sources
- 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.
- Ch7.4: T. Nguyen, "TensorRT-LLM Fundamentals and Quantization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.4. ISBN: 9798244538229.