A New Framework for Evaluating Voice Agents (EVA)

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. We release EVA with an initial airline dataset of 50 scenarios covering flight rebooking, cancellation handling, vouchers, and more — the first in a planned series of domains.

We also provide benchmark results for 20 cascade and audio-native systems, such as speech-to-speech (S2S) models and Large Audio Language Models (LALMs). Our biggest finding is that there is a consistent Accuracy-Experience tradeoff; agents that perform well on task completion tend to deliver worse user experiences, and vice versa.

EVA simulates multi-turn spoken conversations over live audio in which the agent must invoke appropriate tools, adhere to task-specific policies, and reach a deterministically verifiable end state. EVA evaluates voice agents using a bot-to-bot audio architecture composed of five core components:

Additionally, we identified named entity transcription as a dominant failure mode. A single misheard character can cascade into an authentication failure and a full conversation breakdown. Also, multi-step workflows break agents in predictable ways. Rebooking a flight while preserving ancillary services — seats, baggage — is the dominant complexity breaker across all configurations. Finally, we observed that additional calibration is needed for real-world use cases. The gap between pass@3 and pass^3 is substantial across all configurations. Even agents that can complete a task often cannot do so consistently, which is critical for real-world success.

Framework — The user simulator relies on a single commercial provider whose voice characteristics may systematically favor certain ASR systems, and the bot-to-bot pipeline — including audio format conversions and real-time audio interfaces — may not fully represent production deployments. Also, full reproduction requires commercial API access, and latency measurements will vary across providers and infrastructure.

We also thank Lindsay Brin, Akshay Kalkunte, Joseph Marinier, Jishnu Nair, and Aman Tiwari for their careful data review and thoughtful contributions to the framework, and Fanny Riols, Anil Madamala, Sridhar Nemala, and Srinivas Sunkara for their management, leadership, and support throughout. We also extend our thanks to the PAVA and CLAE ServiceNow teams, whose prior work on evaluations and voice agents provided valuable inspiration for this project.

The field currently lacks a framework that evaluates the full quality of voice agent interactions, as most existing efforts assess individual components in isolation. For example, AudioBench , SD-Eval , VoxEval , Kimi-Audio-Evalkit , VoiceBench and VoxDialogue evaluate core speech understanding capabilities for Speech-to-Text (STT) — transcription, paralinguistics, acoustic cues — but remain confined to single-turn, non-interactive settings. On the other hand, EmergentTTS-Eval and SHEET assess perceived speech quality using subjective listening tests (e.g., Mean Opinion Score). Beyond speech perception, FD-Bench , Talking Turns , Full-Duplex-Bench provide deeper analyses of conversational dynamics — interruptions, backchanneling, turn-taking — yet evaluate these in isolation from task-oriented tool use, leaving the relationship between dialogue quality and agentic capability unexamined. More recent efforts, notably VoiceAgentBench and CAVA , take steps towards evaluating the agentic capabilities of commercial voice agent systems, including tool-calling and complex instruction-following. However, these voice-agentic capabilities are not evaluated within complete conversational workflows that voice agents must navigate in practice: from initial user request through multi-step tool orchestration to final task resolution.

The lack of frameworks that jointly capture accuracy and experience underscores the need for a framework that treats voice agent quality as an integrated whole. This means evaluating not only whether the task succeeded, but whether the agent communicated accurately, concisely, and naturally throughout, and surfacing how these dimensions trade off against one another in realistic deployment conditions.

End-to-end evaluation reveals interaction dynamics that are not apparent at the component level: whether the agent interrupts users during natural pauses in speech, whether it recovers smoothly when a user corrects a transcription error, or whether high latency disrupts the conversational flow enough to prompt users to repeat themselves or abandon the task entirely.