Voice agent pipelines hinge on latency and interruption handling

Production AI voice agents depend on coordinated latency budgets across STT, LLM and TTS stages. Callers start noticing lag near 800 ms of total round-trip time, while a 2025 observational study of nine voice-assistant users measured a 1,366 ms mean response delay. STT delay often comes from endpointing rather than compute, with VAD and endpointing estimated at 150-400+ ms, while LLM generation can consume most of the budget before synthesis begins.

Streaming design is central to keeping conversations responsive. Systems can maintain replaceable interim transcript hypotheses alongside committed records, then use turn-detection signals to decide when to hand off to the LLM. Starting LLM prefill on an eager end-of-turn signal can trim 100-200 ms from the critical path, but it may drive 50-70% more LLM calls.

Interruption handling is a major architectural divide. Deepgram’s Flux TTS /v2/speak can return audio_played_ms, text_spoken and text_remaining after an interrupt, reducing the need for clients to reconstruct what a caller heard. Bundled APIs reduce handoffs and connection management, while composed stacks preserve model choice, per-stage metrics and independent deployment controls for teams that need them.