5 Voice Agent Failure Modes You'll Hit in Week One — Venky B, Plivo
Voice agents trade reasoning time for latency, then fail on transcription, data capture, and pronunciation. Treat inputs as typed fields, validate them, and normalize speech output yourself.
Frontier models can take **450–500 ms** to produce a first token at median and exceed **1.2–1.3 seconds** in the tail. Transcription benchmarks report **4–6% word error**, with noisy, accented, or domain-heavy calls often reaching double digits.
Define collected values as typed fields with validation and confirmation instead of asking an LLM to recover structure from a transcript. Plivo reports data-capture accuracy rising from **30% to 95%** with this approach; normalize dates, currency, emails, and pronunciation before TTS as well.
Frontier models can take **450–500 ms** to produce a first token at median and exceed **1.2–1.3 seconds** in the tail. Transcription benchmarks report **4–6% word error**, with noisy, accented, or domain-heavy calls often reaching double digits. Define collected values as typed fields with validation and confirmation instead of asking an LLM to recover structure from a transcript. Plivo reports data-capture accuracy rising from **30% to 95%** with this approach; normalize dates, currency, emails, and pronunciation before TTS as well. Those figures come from Plivo’s production observations and examples, not a controlled comparison across complete voice stacks. The talk also ran short on detail for turn detection, interruption, and backchanneling.
This turns typed contracts and deterministic validation into a voice-specific reliability pattern: unreliable transcription and latency make free-form transcript recovery a poor boundary for collecting consequential values. It reinforces prior harness guidance while adding normalization before speech output, but its production figures do not establish gains for complete voice stacks or resolve turn-taking behavior.