Agentic System Golden Set Dataset
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An Agentic System Golden Set Dataset is an evaluation dataset that contains representative task snapshots and expected trajectorys for agentic system regression testing.
- AKA: Golden Dataset for Agents, Agent Benchmark Dataset, Regression Test Reference Dataset, Canonical Agent Test Set.
- Context:
- It can typically preserve agent interaction trajectorys with environmental states and decision sequences for replay testing.
- It can typically include diverse scenario coverage spanning edge cases, typical usage patterns, and failure modes.
- It can typically maintain version-controlled snapshots with expected outputs and acceptable variance thresholds.
- It can often incorporate human-validated responses as ground truth references for quality assessment.
- It can often support incremental dataset growth through production trace sampling with curation processes.
- It can often enable performance baseline establishment for regression detection and improvement measurement.
- It can range from being a Small Golden Set to being a Comprehensive Golden Set, depending on its coverage scope.
- It can range from being a Static Golden Set to being an Evolving Golden Set, depending on its update frequency.
- It can range from being a Single-Domain Golden Set to being a Multi-Domain Golden Set, depending on its application breadth.
- It can range from being a Synthetic Golden Set to being a Production-Derived Golden Set, depending on its data source.
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- Examples:
- LLM Agent Golden Datasets, such as:
- RAG System Golden Datasets, such as:
- Task-Specific Golden Datasets, such as:
- ...
- Counter-Examples:
- Random Test Dataset, which lacks curation and quality validation.
- Training Dataset, which serves model development rather than regression testing.
- Synthetic Benchmark, which may not reflect real-world scenarios.
- See: Golden Dataset, Regression Testing, Test Dataset Management, Agentic System Regression Testing Task, Benchmark Dataset, Evaluation Dataset, Test Data Curation.