How does a voice AI agent work?
A voice AI agent turns a phone call or microphone stream into a back-and-forth conversation by running a continuous loop: it listens, understands, decides, and speaks — many times per conversation, with each turn happening in well under a second.
When the user speaks, audio streams to a speech-to-text engine that transcribes it in real time. The transcript goes to a large language model, often with retrieved context or tools, which decides what to say or do. The response is sent to a text-to-speech engine that generates natural audio, streamed back to the user. Meanwhile the agent must handle interruptions — when a user starts speaking over the agent (a "barge-in"), the system has to stop talking and listen.
Getting this loop to feel natural, not robotic, is the entire engineering challenge.
What's in the voice AI stack?
A production voice AI agent is an orchestration of specialised components, not a single model.
| Layer | Job | Common tools |
|---|---|---|
| Transport | Stream audio in real time | WebRTC, LiveKit |
| Speech-to-text (STT) | Transcribe the user | Deepgram, AssemblyAI |
| Reasoning (LLM) | Decide the response | GPT, Claude, fine-tuned models |
| Text-to-speech (TTS) | Generate the reply audio | ElevenLabs, Azure |
| Orchestration | Manage turns, interruptions, state | LiveKit Agents, custom |
Each layer adds latency, and the layers run in sequence on every turn — which is why architecture, not any single model, determines whether the agent feels responsive.
Why is latency the hardest part?
Latency is the defining problem of voice AI because humans notice conversational delay almost instantly. A response that takes two seconds feels broken, even if every word is correct. Since the STT → LLM → TTS chain runs on every turn, each component's delay stacks, and a slow database query or an extra network hop anywhere in the loop is felt by the caller.
Prodinit faced exactly this scaling a sales-simulation platform's voice AI: we migrated it from a coupled Django WebSocket monolith to a self-hosted LiveKit agent architecture, eliminated every database query over 500ms, and built multi-metric autoscaling — enabling the system to handle 10x peak load while staying responsive.