Voice agent evaluation measures whether a spoken AI system understands callers, responds at the right time, completes the requested task, and recovers from interruptions or noise. It combines conversation quality with operational outcomes, because a natural sounding call still fails when the booking, transfer, or update never happens.
What belongs in a voice agent evaluation?
Test the whole call, not only the generated words. The primary score should reflect the caller’s completed outcome, supported by measures for transcription, tool use, turn detection, latency, and escalation. A failed action must remain a failure even when the conversation sounds polished. Use representative phone audio, accents, pauses, and noisy environments rather than studio recordings alone.
How should teams run these evaluations?
Start with fixed scenarios for regression testing, then add simulated-user evaluation and sampled production reviews. Track task completion rate separately from subjective speech quality, so a pleasant voice cannot hide operational errors. Compare results by scenario and failure type, and preserve call traces so teams can tell whether voice agents failed in speech recognition, reasoning, a connected tool, or handoff.
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