SynAE: A Framework for Measuring the Quality of Synthetic Data for Tool-Calling Agent Evaluations
arXiv:2605.22564v1 Announce Type: cross Abstract: Today, tool-calling agents are commonly evaluated or tested on static datasets of execution traces, including input commands, agent responses, and associated tool calls. However, internal production datasets are often insufficient or unusable for testing; for example, they may contain sensitive or proprietary data, or they may be too sparse to support comprehensive testing (especially pre-deployment). In these settings, practitioners are increasingly replacing or augmenting real datasets with synthetic ones for evaluation purposes. A key challe
The rapid advancement and deployment of AI agents necessitate robust evaluation methods, yet real-world data limitations are becoming a critical bottleneck, pushing the need for synthetic data solutions.
Evaluating tool-calling agents effectively is crucial for their safe and reliable deployment across various industries, making the quality of evaluation data a core concern for AI development.
The focus shifts from simply using synthetic data to actively measuring and improving its quality for AI agent evaluations, potentially accelerating agent development and deployment cycles.
- · AI development platforms
- · Agentic AI companies
- · Synthetic data providers
- · Companies adopting AI agents
- · Companies relying solely on real-world data for agent testing
- · AI evaluation companies lacking synthetic data expertise
Improved synthetic data quality leads to more rigorous and efficient testing of AI agents.
Faster, more reliable agent development cycles accelerate the deployment of advanced AI applications across industries.
The widespread adoption of high-quality synthetic data for testing could reduce reliance on proprietary real-world datasets, democratizing access to agent development and potentially lowering barriers to entry.
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