> tpl_air_051
Agent Evaluation Dataset and Scenario Catalogue
Governed benchmark repository and test scenario catalogue documenting task complexity tiers, edge-case injections, user persona variants, multi-turn reasoning traps, and verified ground-truth trajectories for continuous agent regression testing.
Benchmark repository and scenario catalogue documenting task complexity, edge cases, user personas, and verified ground-truth trajectories.
Important Tech Document Template & Operational Notice
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Problem Solved
AI engineers test autonomous agents against informal, unversioned prompts that fail to cover edge cases, adversarial inputs, or multi-step reasoning deadlocks, causing silent regressions with every prompt update.
When to Use
- •Assembling, structuring, and versioning golden evaluation datasets for multi-agent systems
- •Curating diverse task scenarios spanning beginner users, ambiguous queries, and malicious prompt injections
- •Establishing ground-truth execution trajectories for automated CI/CD agent regression scoring
When NOT to Use
- •For broad corporate data cataloguing and metadata discovery (use TPL-AIM-004)
- •For live execution trajectory tracing and telemetry monitoring (use TPL-AIR-044)
5 Template Sections & Structural Outline
Stratifying scenarios into difficulty tiers: Level 1 (Single-tool deterministic lookup), Level 2 (Multi-tool sequential synthesis), Level 3 (Ambiguous user intent requiring clarification), and Level 4 (Adversarial edge-case attack).
Sourcing realistic test cases from anonymized customer support logs, production postmortems (TPL-AIR-048), and domain expert interviews. Eliminating synthetic bias.
Documenting the verified optimal reasoning path for each scenario: prompt input, expected intermediate tool invocations with exact arguments, and allowable final response semantic criteria.
Injecting varied user styles: highly technical shorthand, colloquial non-native English, emotionally frustrated language, and fragmented multi-message inputs.
Versioning datasets using DVC or Hugging Face Git LFS. Ensuring evaluation test cases are strictly quarantined from foundation model training and fine-tuning pipelines.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
Agent Evaluation Dataset and Scenario Catalogue - Worked Case Study
Fictional Entity: Enterprise E-Commerce Operations Multi-Agent Benchmark (500 Golden Trajectory Scenarios)
Real-world production case study demonstrating complete operational adoption for Enterprise E-Commerce Operations Multi-Agent Benchmark (500 Golden Trajectory Scenarios).
- •Curated 500 gold-standard agentic scenarios spanning 12 distinct customer personas and 4 difficulty tiers
- •Detected a 16% accuracy regression in multi-order cancellation flows before shipping a model parameter update
- •Quarantined benchmark datasets using DVC cryptographic hashes, eliminating data contamination risks
Frequently Asked Questions
Why must golden evaluation datasets include negative and ambiguous test scenarios?
If a dataset contains only well-formed, solvable tasks, the agent will learn to guess or execute unvetted tools when faced with confusing real-world queries. Ambiguous scenarios test whether the agent possesses the capability to pause, ask clarifying questions, or refuse unsafe requests.
How does data contamination threaten the validity of agent benchmark suites?
If evaluation test cases leak into the training corpora of foundation models or fine-tuning pipelines, the agent simply memorizes the correct answers and optimal tool sequences. Contamination creates the false illusion of high competence while collapsing in production.
What is the role of Data Version Control (DVC) in agent benchmark engineering?
DVC tracks multi-gigabyte evaluation datasets alongside git source code using cryptographic content hashes. This guarantees that when a regression occurs, engineers can run the exact identical benchmark version used three months prior to prove whether the model regressed.
Download Tech Document Pack
Auth RequiredDownload all blank templates, worked scenarios, and verification manifests in a single verified archive.
Authoritative Sources
- Datasheets for Datasets (Communications of the ACM, Gebru et al.)ACM • OFFICIAL REQUIREMENT
- Data Version Control (DVC) DocumentationIterative.ai • OFFICIAL REQUIREMENT
- NIST AI 100-1: Artificial Intelligence Risk Management FrameworkNIST • OFFICIAL REQUIREMENT