A Low-Latency Architecture for Voice Agents with Real-time Web Search

The development of real-time voice agents involves a critical tradeoff between latency and intelligence, as responses must occur within a strict time frame to maintain conversational flow, typically targeting a sub-second time-to-first-audio response. Traditional approaches use small, fast models that sacrifice depth for speed, leading to limited reasoning and higher hallucination rates. The architecture discussed in the text addresses this challenge by employing a fast Mixture-of-Experts (MoE) model and a conditional retrieval strategy, utilizing a large parameter model (Qwen3.6-35B-A3B) for superior reasoning capabilities while maintaining low latency. The system integrates Linkup's optimized search API to provide real-time information, though this introduces additional latency, mitigated by a user experience technique that uses spoken fillers to cover retrieval delays. This combination enables the deployment of voice agents that maintain conversational latency without compromising on the quality of information, offering a refined balance between speed and intelligence in voice interactions.

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