Latency in voice AI: why responsiveness matters

Ask anyone what makes an AI phone call feel "off" and they'll often say the voice — but the real culprit is usually latency: the gap between the caller finishing speaking and the agent responding. Conversations have a rhythm, and breaking it breaks the illusion.

The rhythm of conversation

Natural human conversation has turn-taking gaps of roughly 200–500 milliseconds — nearly instant. Callers notice delays over about a second as awkwardness, and delays over two seconds as something's wrong. Long pauses make callers repeat themselves, talk over the agent, or hang up.

Where the latency comes from

The pipeline has four stages, each adding time:

1. End-of-speech detection — knowing the caller finished (harder than it sounds: pauses mid-sentence vs. actual endings).
2. Speech recognition — transcribing the audio to text.
3. The model — generating the reply, possibly calling tools.
4. Speech synthesis — rendering the reply to audio.

Every stage can stream — transcribing while they speak, generating while transcribing, synthesising while generating — which is how total latency gets under a second.

What good systems do about it

• Streaming everything — no waiting for completed stages.
• Fillers under load — "let me check that for you" while a calendar query runs.
• Fast models for fast turns — the model that answers the phone is optimised for speed as much as cleverness.
• Barge-in handling — callers interrupt; the agent stops gracefully.

What this means for you

Callers judge the agent by feel, and feel is mostly timing. Agentric's voice layer is engineered around keeping the conversational rhythm — it's the difference between "surprisingly good" and "still feels like a robot."