The Voice Agent Architecture Guide: STT, LLM, and TTS Pipeline Design in 2026

AI voice agent performance depends on managing the combined latency of speech-to-text (STT), language model (LLM), text-to-speech (TTS), transport, endpointing, and interruption handling rather than optimizing any stage independently. The guide notes that callers may notice delays near 800 ms, while STT endpointing and LLM time-to-first-token often consume most of the response budget; streaming partial transcripts, speculative LLM prefill after early turn signals, and token-by-token TTS can reduce perceived pauses. It compares bundled, single-WebSocket voice-agent APIs with composed stacks of separate STT, LLM, and TTS services: bundled systems reduce connections, handoff races, and some interruption-tracking work, while composed systems offer wider model choice, self-hosting, compliance isolation, and per-stage observability. Interruptions require careful reconciliation between audio actually played and text not heard, with newer TTS APIs able to return spoken and remaining text to simplify conversation-state updates. Production planning should measure p95 latency under realistic traffic, test barge-ins and reconnections, account for session and rate limits, validate partial-failure behavior, and select an architecture based on whether operational simplicity or component-level control provides greater value.

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