Episode Details

This story was originally published on HackerNoon at: https://hackernoon.com/best-voice-agent-evaluation-and-testing-tools-in-2026.
Learn how to evaluate AI voice agents using simulation, monitoring, STT benchmarks, semantic WER, latency, entity accuracy, and task completion.
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Voice agents pass the demo and fail on call 4,000. This is a neutral guide — written from the speech layer underneath, not by an eval vendor — to the tools that catch those failures: commercial platforms like Coval, Hamming, Cekura, Maxim, and Roark; general LLM-eval tools; and the free open benchmarks (Daily's Pipecat, Coval, Hugging Face Open ASR). It covers the metrics that actually matter — semantic WER, TTFS and P95 latency, entity accuracy, tool-call success, and task completion — and why every evaluation starts with getting the transcript right.