The Problem With Production Data for Agent Testing

Tool-calling agents are commonly evaluated against static datasets of execution traces, which include input commands, agent responses, and associated tool calls. In practice, however, the internal production datasets that would be most relevant for this work are frequently off-limits or inadequate. They may contain sensitive or proprietary information, or they may be too sparse to support comprehensive testing, particularly in pre-deployment scenarios [1].

Those constraints have pushed practitioners toward synthetic datasets as replacements or supplements for real data. The substitution introduces a new problem: there has been no standard way to measure how closely a synthetic dataset mirrors the real data it is meant to stand in for. SynAE is designed to fill that gap.

What SynAE Is

SynAE is an evaluation framework built specifically to assess how well synthetic benchmarks for multi-turn, tool-calling agents replicate and augment the characteristics of real data trajectories [1]. The framework operates across multiple measurement axes, evaluating three core properties of synthetic data: validity, fidelity, and diversity.

The scope is deliberately broad. SynAE covers full agent trajectories rather than isolated outputs, making it applicable to the kinds of complex, multi-step interactions that characterize production tool-calling agents.

Four Metric Categories

SynAE organizes its measurements into four categories, each targeting a distinct aspect of agent behavior [1].

The first category covers task instructions and intermediate responses, assessing whether the synthetic data reproduces the structure and content of the prompts and replies that occur throughout an agent’s execution trace. The second category focuses on tool calls, examining how faithfully synthetic data captures the patterns of tool invocation that appear in real agent workflows.

The third category addresses final outputs, measuring whether the end results produced by agents operating on synthetic data resemble those produced on real data. The fourth category is downstream evaluation, which examines how synthetic datasets perform when used as inputs to evaluation pipelines, the ultimate test of whether a synthetic benchmark is fit for purpose [1].

How SynAE Detects Synthetic Data Failure Modes

To validate the framework, the researchers evaluated SynAE against recent agent benchmarks and tested common synthetic data failure modes using both realistic and controlled generation schemes [1]. The controlled approach allows the framework to isolate specific variables, while the realistic scheme tests performance under conditions closer to what practitioners would encounter in practice.

The results show that SynAE can detect fine-grained variations in data validity, fidelity, and diversity across these generation conditions [1]. That sensitivity is significant because subtle failures in synthetic data quality, such as a narrow distribution of tool call patterns or instructions that drift from real-world phrasing, can compromise evaluation results without being obvious from a surface inspection.

Why No Single Metric Is Enough

A central finding of the SynAE paper is that no individual metric is sufficient to fully characterize synthetic data quality [1]. A dataset might score well on task instruction fidelity while exhibiting low diversity in tool call sequences, or it might produce plausible final outputs while failing on downstream evaluation metrics.

This finding motivates the multi-axis design of the framework. Relying on a single proxy measure risks missing failure modes that only become visible when multiple dimensions are examined together. The four-category structure is a direct response to that limitation.

Practical Implications for Agent Evaluation Teams

SynAE is aimed at practitioners who build and maintain evaluation pipelines for tool-calling agents, particularly those operating in environments where production data is restricted or insufficient [1]. Teams preparing for deployment can use the framework to audit synthetic benchmarks before committing to them, reducing the risk that evaluation results will fail to generalize to real-world conditions.

A public demo of SynAE and its source code are available at the addresses referenced in the paper [1]. The availability of both a demo and an open codebase lowers the barrier for evaluation teams to integrate the framework into existing workflows without building measurement tooling from scratch.

FAQ

Q. What types of agents does SynAE apply to? SynAE is designed for multi-turn, tool-calling agents evaluated on static datasets of execution traces that include input commands, agent responses, and tool calls [1].

Q. Does SynAE require access to production data to function? The framework is built to address situations where production data is sensitive or sparse, but it does measure how closely synthetic data matches real data trajectories, so some reference data is implied in the fidelity and validity assessments [1].

Q. Can SynAE identify which specific dimension of a synthetic dataset is failing? Yes. The four-category structure allows SynAE to detect fine-grained variations across validity, fidelity, and diversity, making it possible to isolate whether a failure is in tool calls, task instructions, final outputs, or downstream evaluation performance [1].

Q. Is SynAE available for immediate use? A demo and the underlying code are publicly available, with links provided in the paper [1].

Q. Why is multi-axis evaluation necessary rather than a single composite score? The paper’s findings show that no single metric fully characterizes synthetic data quality, meaning a composite score could mask failures in specific dimensions that would affect real evaluation outcomes [1].

Key takeaways

  • SynAE addresses the absence of a standard method for measuring how well synthetic datasets replicate real data for tool-calling agent evaluation.
  • The framework evaluates three properties, validity, fidelity, and diversity, across four categories: task instructions and intermediate responses, tool calls, final outputs, and downstream evaluation.
  • SynAE was tested against recent agent benchmarks using both controlled and realistic synthetic generation schemes, and demonstrated sensitivity to fine-grained data quality variations.
  • The paper finds that no single metric is sufficient to characterize synthetic data quality, supporting a multi-axis evaluation approach.
  • Both a public demo and open-source code are available, enabling evaluation teams to adopt the framework without building measurement infrastructure from scratch.