5 Voice Agent Failure Modes You'll Hit in Week One — Venky B, Plivo
This talk details five critical failure modes encountered when deploying voice AI agents from a proof-of-concept (PoC) environment to production scale. The core message emphasizes that successful production deployment requires shifting focus from end-to-end transcript processing to structured data collection, rigorous latency management, and building robust normalization layers between the LLM and Text-to-Speech (TTS) components. Key technical recommendations include using smaller, self-hosted open-source models for low latency, treating data collection as unit-testable field validation, and normalizing all output before synthesis.
Key takeaways
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Latency and Time to First Audio (TTFA)
9:54
Industry average TTFA often falls between 750ms and 1,200ms, leading to user drop-off. To improve this, the speaker recommends using smaller, self-hosted open-source models (e.g., Quen 3.5, Gemma 4) to target under 300ms, balancing cost, intelligence, and latency.
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Data Collection Must Be Structured
19:20
Instead of treating input as a single transcript, data collection should be modeled like structured fields (e.g., using Pydantic or Zod). This approach significantly increases accuracy (from ~30% to mid-90s) by allowing validation and error handling per field (e.g., phone numbers, addresses).
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Normalization Layer is Critical
24:30
Never feed raw LLM output directly to a TTS engine. A dedicated normalization layer must be implemented to strip markdown/emojis, apply custom pronunciation dictionaries, and control speaking speed (e.g., 0.7x or 0.8x) to ensure proper pronunciation of entities.
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Transcription is Brittle by Default
20:40
Transcription engines struggle with proper nouns, jargon, and code-switched languages. Solutions include implementing dynamic keyword boosting and post-processing the raw transcript using an LLM layer, which provides necessary domain context.