Agent Evaluation Datasets
Does the evaluation represent the work and failures that matter in deployment?
Reference notes by Min · Reviewed October 15, 2026
Definition
An agent evaluation dataset is a versioned collection of tasks, operating conditions and expected outcomes used to examine a workflow. It may include tool state, permissions, intermediate events and side effects rather than only prompts and reference answers.
The dataset defines what a score can support. If every case is a short English request with a responsive tool, the results provide little direct evidence about other languages, long conversations or service failures. Describe exclusions before presenting a number as deployment readiness.
What it covers
- Coverage
- Represent important tasks, users, languages and failure conditions.
- Ground truth
- Define acceptable outcomes, prohibited actions and uncertain labels.
- Separation
- Keep final evaluation cases apart from development cases.
- Maintenance
- Version cases and explain changes to score comparisons.
Correctness can include doing nothing. Refusal or clarification may be appropriate when authority or information is missing. Record the final response and externally visible effects that determine whether the workflow respected its constraints. Allow multiple acceptable paths when the task genuinely permits them.
In practice
Practical design patterns by Min.
- Map cases to consequential risks and common tasks.
- Include permissions, tool responses and expected side effects.
- Review ambiguous labels with domain experts.
- Report results by task class as well as overall.
Maintain a case record with source, permission to use the data, task class, environment state, expected behavior, prohibited effects and reviewer notes. Synthetic cases can be useful, but compare their structure with the intended workload. Preserve a held-out set for decisions after development changes.
Research notes
Source-grounded details and editorial applications. Follow each link for the original document.
Document a dataset’s intended use
Gebru and colleagues propose datasheets describing motivation, composition, collection and intended uses. This article applies that documentation approach to agent evaluation cases and their limits.
Source: Gebru et al.: Datasheets for Datasets (CACM 2021; author manuscript) ↗
Measure relevant risks in context
NIST AI RMF Measure emphasizes evaluation of identified risks in context. A workflow-specific dataset can support that work, while a benchmark score alone cannot establish safety in an untested deployment.
Working example
Evaluating a refund workflow
A team evaluates eligible, ineligible and uncertain refund requests.
- Create authorized test records for normal requests, policy exceptions and service failures.
- Specify when completion, refusal, clarification or escalation is expected.
- Inspect payment state so a fluent message cannot hide an unauthorized or duplicate refund.
Why the controls matter
A correct refund explanation paired with two payments is a failed case. An uncertain service response should exercise reconciliation rather than reward a confident success claim. Repeated runs can expose variability; report run conditions and attempt counts alongside the result.
Common pitfalls
- Testing only prompts with an obvious answer.
- Repeatedly tuning on the final set while calling it independent evidence.
- Comparing dataset versions without explaining changed cases.
A large set can repeat the same easy pattern. Review which failure mechanisms are represented and whether critical cases are hidden by common successes. When incidents produce new cases, version the dataset and explain how added coverage changes the interpretation of performance trends.
Questions for operational review
Use these questions to decide what evidence your team needs. They are editorial prompts, not a compliance checklist.
- Which deployment conditions are absent?
- Can the evaluator observe prohibited side effects?
- Who reviews expected outcomes and dataset changes?
Articles linking here
References
Primary documents reviewed for this entry. Examples and implementation patterns are editorial synthesis.
- Gebru et al.: Datasheets for Datasets (CACM 2021; author manuscript) ↗Accessed October 15, 2026
- NIST AI RMF Core: Govern, Map, Measure and Manage ↗Accessed October 15, 2026