A Guide to Core Latency in AI Voice Agents (Cascaded Edition)

Latency is a critical factor in designing AI voice agents, as human users naturally find conversational pauses uncomfortable. Twilio has developed a Cascaded Voice Agent Architecture that transcribes speech to text, processes it through a large language model (LLM), and synthesizes it back into speech, each step contributing to overall latency. The guide emphasizes understanding core latency, the time from when a user stops speaking to when the agent's reply reaches their ear, and provides initial latency targets. The architecture's modularity allows for optimization, yet also introduces variability in latency due to factors like network transmission and inter-service delays. Strategies to minimize latency include regional deployment of services, careful orchestration to avoid unnecessary network hops, and choosing appropriate speech-to-text and text-to-speech models. Smart endpoint detection can reduce latency, but risks introducing delays if the system incorrectly predicts user behavior. Twilio's managed service, ConversationRelay, simplifies the complexity of achieving low-latency performance by handling the entire media and AI processing pipeline. The guide underscores the importance of balancing latency with expressive and meaningful voice synthesis to maintain user engagement.