Turn Detection in Voice Agents: The Tradeoff Nobody Talks About

Turn detection — determining when the caller has finished speaking — is the most underrated problem in voice AI. Get it wrong in one direction and the agent talks over the caller. Get it wrong in the other and the agent waits too long, creating awkward silences. Every platform makes a tradeoff, and most don't tell you about it.

The spectrum

Fast endpointing (200–400ms of silence triggers response): the agent feels snappy and responsive. But it interrupts callers who pause mid-thought, take a breath before finishing a sentence, or switch languages mid-utterance. Slow endpointing (800–1200ms): the agent almost never interrupts, but the conversation feels sluggish, like talking to someone on a bad satellite connection. The right setting depends on your use case, your callers, and your tolerance for each failure mode.

Beyond simple timers

Simple VAD (voice activity detection) treats all silence the same. Smarter approaches use semantic signals: is the sentence grammatically complete? Did the intonation fall (statement) or rise (question still in progress)? Is the content a complete thought? Model-based turn detection uses these signals to predict turn completion more accurately than silence duration alone. The tradeoff: more computation per frame, more latency in the detection itself. But the conversation quality improvement is usually worth the few milliseconds.

Configurable, not one-size-fits-all

The ideal turn detection settings for a fast-paced sales qualification call are different from a patient, empathetic healthcare intake call. Your platform should let you configure this per agent or per flow — not impose a global default. In Agent Canvas, turn detection behavior can be set at the node level, allowing different conversation segments to have different sensitivity.