A Low-Latency Architecture for Voice Agents with Live Web Retrieval

Building real-time voice agents involves navigating the tradeoff between latency and intelligence, as these systems must deliver responses quickly to avoid conversational breakdowns. The typical voice pipeline includes stages like speech-to-text (STT), language model reasoning, and text-to-speech (TTS), each adding latency. To address this, teams often use smaller, faster models that may sacrifice accuracy, leading to higher hallucination rates on factual queries. A proposed solution involves using a fast Mixture-of-Experts (MoE) model and conditional web search retrieval to maintain both speed and accuracy. This architecture employs Qwen3.6-35B-A3B, a large parameter MoE model that offers quality at reduced latency, and integrates a fast search API from Linkup for real-time facts. The system employs latency-masking techniques, such as speaking a filler phrase during web searches, to maintain a sense of immediacy in conversations. This approach allows voice agents to deliver timely, grounded responses without compromising on intelligence, ensuring they remain conversationally effective.

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