Agentic System Golden Set Dataset

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.
  • ...
• Examples:
  • LLM Agent Golden Datasets, such as:
    • ChatGPT Interaction Golden Set with conversation historys and expected completions.
    • Code Generation Golden Dataset containing programming tasks with verified solutions.
  • RAG System Golden Datasets, such as:
    • Question-Answer Pair Golden Set with relevant documents and expected retrievals.
    • Multi-Hop Reasoning Golden Dataset for complex query validation.
  • Task-Specific Golden Datasets, such as:
    • Customer Service Agent Golden Set with support tickets and resolution paths.
  • ...
• 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.

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