arXiv:2605.13841v2 Announce Type: replace-cross Abstract: Voice agents, artificial intelligence systems that conduct spoken conversations to complete tasks, are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses two core evaluation challenges: generating realistic simulated conversations, and measuring quality across the full scope of voice-specific failure modes. We present EVA-Bench, an end-to-end evaluation framework that addresses both. On the simulation side, EVA-Bench orchestrates bot-to-bot audio conversations over dynamic multi-turn d
The proliferation of voice agents across enterprise applications necessitates robust, end-to-end evaluation frameworks capable of handling complex conversational dynamics and specific failure modes.
Evaluating voice agents effectively is crucial for their reliable deployment and for understanding their true capabilities and limitations, impacting user experience and strategic investment in AI.
The introduction of EVA-Bench provides a standardized, comprehensive framework for assessing voice agents, allowing for more realistic simulation and identification of nuanced performance issues than previous methods.
- · AI developers
- · Enterprise software providers
- · Voice agent users
- · Companies relying on incomplete evaluation methods
- · Voice agent providers with poor quality control
Improved quality and reliability of voice agents in enterprise applications.
Accelerated development and adoption of sophisticated voice AI, leading to more complex functionalities.
Enhanced automation of customer service and operational workflows through more trustworthy voice interfaces.
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