How to Build Your Own AI Voice Agent
A practical blueprint for creating a real-time voice agent using speech-to-text, an LLM for dialog, and text-to-speech—plus best practices for turn taking, latency, and deployment.
Published: 01 Nov 2025
Core Architecture
A voice agent has three core parts: Speech-to-Text (STT) to transcribe the user, an LLM to plan and respond, and Text-to-Speech (TTS) to speak back. Connect them with a low-latency event loop and stream audio both ways for a natural experience.
- • STT: Live transcription with partial results
- • LLM: Dialog policy, memory, function calls
- • TTS: Streamed speech for fast responses
- • Orchestration: Turn-taking, barge-in, error handling
Turn Taking & Streaming
Use voice activity detection to determine when the user stops talking, and allow "barge-in" to interrupt TTS if the user starts again. Stream partial STT to the LLM and stream TTS back as it’s generated to cut perceived latency.
Dialog Design
Define intents, slots, and actions. Keep a lightweight memory for the session and use function calls to fetch data, send emails, or schedule meetings. Constrain prompts to enforce consistency and safety.
Deployment Channels
Start with the web for rapid iteration, then expand to mobile or telephony. Ensure secure token handling and add logging for metrics such as latency, transcription quality, and user satisfaction.
Recommended Stack
Explore leading STT/TTS with strong latency and quality.- • Frontend: Web Audio API + WebSockets for streaming
- • STT/TTS: ElevenLabs for low-latency voice
- • LLM: GPT‑4o / Claude for robust reasoning and tools
- • Backend: Node/TypeScript for orchestration + actions
Safety & Reliability
Add content moderation, allow-list actions, timeout safeguards, and graceful degradation. Log every hop (STT→LLM→TTS) with timestamps for debugging. Use retries and backoff for transient errors.
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