Synthetic Data for Agent Evaluation
Do invented test records preserve the difficulty of the real task?
Reference notes by Min · Reviewed October 15, 2026
Definition
Synthetic evaluation data consists of constructed examples used to test an agent without directly replaying every real record. Its value depends on whether it preserves relevant task structure and failure modes, and on how any source information used to create it is handled.
Synthetic data can make sensitive or rare scenarios easier to exercise, but it can also encode the assumptions of its creator. If all generated invoices are clean and consistent, the dataset may miss the ambiguity that makes the real workflow difficult.
What it covers
- Origin
- How cases were constructed and from which inputs.
- Realism
- Which operating relationships are preserved.
- Coverage
- Which edge cases are deliberately included.
- Privacy
- Whether generated content can expose source information.
Specify which properties matter: record relationships, distribution, language, missing fields or adversarial content. The goal is not visual realism alone. A plausible-looking example may violate business constraints and produce misleading judgments about agent performance.
In practice
Practical design patterns by Min.
- Document generation methods and source-data permissions.
- Review cases with people familiar with the real workflow.
- Compare synthetic results with appropriately governed representative evidence.
Maintain generation version, reviewer decisions and known limitations with the dataset. Use independent checks for consequential cases. When real records inform generation, evaluate privacy exposure and avoid asserting anonymity solely because values were modified or produced by a model.
Research notes
Source-grounded details and editorial applications. Follow each link for the original document.
Document construction and limits
Datasheets for Datasets proposes documenting composition, collection and intended use. The same transparency helps readers interpret a synthetic evaluation set.
A privacy claim needs a defined guarantee
NIST SP 800-226 explains differential privacy and implementation hazards. It does not make arbitrary synthetic data automatically private.
Source: NIST SP 800-226: Evaluating Differential Privacy Guarantees ↗
Working example
Testing refund rules without customer records
A team creates fictional orders covering eligibility and service failures.
- Construct dates, amounts and statuses that obey realistic relationships.
- Include ambiguous and ineligible requests.
- Check that the evaluation rewards correct action or escalation, not superficial phrasing.
Why the controls matter
The refund test should include an unavailable service and an uncertain payment outcome. Those conditions examine workflow behavior that polished question-answer examples omit. Review whether the expected result is objectively supported by the constructed record.
Common pitfalls
- Assuming fictional names make source-derived records anonymous.
- Generating every case with the same model being evaluated.
- Making synthetic tasks easier than actual production work.
Differential privacy is a specific mathematical approach with assumptions and implementation requirements. Do not attach that label to ordinary synthetic generation. If a privacy guarantee is claimed, identify its mechanism, parameters and scope and obtain appropriate technical review.
Questions for operational review
Use these questions to decide what evidence your team needs. They are editorial prompts, not a compliance checklist.
- Which real failure conditions does the synthetic set preserve?
- Who independently reviews generated cases?
- What supports any privacy guarantee?
Articles linking here
References
Primary documents reviewed for this entry. Examples and implementation patterns are editorial synthesis.
- Gebru et al.: Datasheets for Datasets ↗Accessed October 15, 2026
- NIST SP 800-226: Evaluating Differential Privacy Guarantees ↗Accessed October 15, 2026