Why Your AI Voice Agent Feels Slow — And It's Not the Model

Developers and businesses often blame the AI model when a voice agent feels sluggish, but the real culprit is usually elsewhere in the processing chain. Turn latency — the gap a caller experiences between finishing their sentence and hearing the agent respond — is the sum of multiple steps including endpointing, transcription, tool calls, and speech generation. Endpointing, the system's judgment of when a caller has stopped speaking, is frequently the largest single contributor to perceived slowness and is often misconfigured as a single global constant. Applying shorter wait times for brief confirmations and longer waits for open-ended questions or digit-by-digit recitations resolves most complaints without touching the model at all. Treating latency as a property of the entire pipeline rather than a single component metric is the key shift needed to build voice agents that feel genuinely responsive.