5 Voice Agent Failure Modes You'll Hit in Week One — Venky B, Plivo

Transitioning voice AI agents from proof-of-concept to production requires addressing critical failure modes in latency, transcription accuracy, and data collection. Achieving optimal performance involves balancing cost and intelligence by utilizing open-source models like Qwen 3.5 or Gemma 4, which offer superior control over latency compared to frontier models. Transcription brittleness is mitigated through dynamic keyword boosting and LLM-based post-processing to normalize noisy inputs like phone numbers and proper nouns. Furthermore, treating data collection as a structured schema problem—similar to form validation in software development—significantly improves accuracy by constraining inputs. Finally, implementing a robust normalization layer between the LLM and text-to-speech engine ensures consistent pronunciation and formatting, ultimately creating a more reliable and repeatable user experience for voice-based agents.