A New Framework for Evaluating Voice Agents (EVA)

A New Framework for Evaluating Voice Agents (EVA) A New Framework for Evaluating Voice Agents (EVA) Enterprise Article Published March 24, 2026 Upvote 97 Tara Bogavelli tarabogavelli ServiceNow-AI Gabrielle Gauthier Melancon gabegma ServiceNow-AI Katrina Stankiewicz kstankiewicz ServiceNow-AI Nifemi Bamgbose onifemibam ServiceNow-AI Hoang Nguyen hnguy7 ServiceNow-AI Raghav Mehndiratta rmehndir ServiceNow-AI Hari Subramani Hari-sub ServiceNow-AI Fanny Riols FannyRiols ServiceNow-AI Introduction Conversational voice agents present a distinct evaluation challenge: they must simultaneously satisfy two objectives — accuracy (completing the user's task correctly and faithfully) and conversational experience (doing so naturally, concisely, and in a way appropriate for spoken interaction). These objectives are deeply intertwined: mishearing a confirmation code renders perfect LLM reasoning meaningless, a wall of options overwhelms a caller who can't skim spoken output, and delayed responses can pass every accuracy check while remaining unusable in practice.

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• A New Framework for Evaluating Voice Agents (EVA) Enterprise Article Published March 24, 2026 Upvote 97 Tara Bogavelli tarabogavelli ServiceNow-AI Gabrielle Gauthier Melancon gabegma ServiceNow-AI Katrina Stankiewicz kstankiewicz ServiceNow-AI Nifemi Bamgbose onifemibam ServiceNow-AI Hoang Nguyen hnguy7 ServiceNow-AI Raghav Mehndiratta rmehndir ServiceNow-AI Hari Subramani Hari-sub ServiceNow-AI Fanny Riols FannyRiols ServiceNow-AI Introduction Conversational voice agents present a distinct evaluation challenge: they must simultaneously satisfy two objectives — accuracy (completing the user's task correctly and faithfully) and conversational experience (doing so naturally, concisely, and in a way appropriate for spoken interaction).
• These objectives are deeply intertwined: mishearing a confirmation code renders perfect LLM reasoning meaningless, a wall of options overwhelms a caller who can't skim spoken output, and delayed responses can pass every accuracy check while remaining unusable in practice.
• Existing frameworks treat these as separate concerns — evaluating task success or conversational dynamics, but not both.
• We introduce EVA, an end-to-end evaluation framework for conversational voice agents that evaluates complete, multi-turn spoken conversations using a realistic bot-to-bot architecture.
• EVA produces two high-level scores, EVA-A (Accuracy) and EVA-X (Experience), and is designed to surface failures along each dimension.
• EVA is the first to jointly score task success and conversational experience.