voice-agents

Voice Agents - Voice AI Agent Architecture Skills

Skill Overview

The Voice Agents skill provides architectural design and implementation guidance for voice AI systems. It covers two architecture patterns—speech-to-speech and pipeline—helping developers build natural voice dialogue systems with latency below 800 ms.

Suitable Scenarios

1. Customer Service Voice Bot

Build an intelligent customer service voice system that handles a large volume of incoming calls. It supports natural conversation, barge-in interruptions, and emotion preservation, delivering a near-human calling experience.

2. Real-Time Voice Assistant

Develop a voice AI assistant application to enable low-latency voice interaction. It is suitable for voice control scenarios in smart devices, in-car systems, or mobile applications.

3. Phone Voice AI Systems

Create phone automation solutions for scenarios such as appointment confirmation, information lookup, and order processing. It supports stable long-duration calls and background noise handling.

Core Features

1. Support for Two Architecture Modes

Provide two architecture options: Speech-to-Speech (S2S) and Pipeline (STT→LLM→TTS). The S2S mode achieves the lowest latency and emotion preservation through the OpenAI Realtime API. The Pipeline mode offers stronger controllability and easier debugging.

2. Latency Optimization Strategies

Systematic latency budget management covering each stage, including VAD (Voice Activity Detection), transmission, and processing. The goal is to keep end-to-end latency within 800 ms to ensure natural and fluent conversations.

3. Dialogue Interaction Control

Implement voice activity detection, turn-taking, and barge-in detection. Handle edge cases such as background noise and STT errors to build a stable voice interaction experience.

Frequently Asked Questions

What is the ideal latency for a voice agent?

Natural voice conversation requires end-to-end latency below 800 ms. Beyond 1 second, it feels noticeably awkward; beyond 1.5 seconds, the user experience drops significantly. The latency budget must be allocated to each stage, such as VAD detection, audio transmission, model inference, and TTS synthesis.

How do I choose between Speech-to-Speech and Pipeline architectures?

Speech-to-Speech (e.g., the OpenAI Realtime API) is suitable for scenarios that require the lowest latency and emotion preservation, but it offers weaker controllability. In the Pipeline architecture, STT, LLM, and TTS are separated, so each step can be independently controlled and debugged. It is suitable for scenarios requiring fine-grained logic handling, but it has higher latency.

How does a voice agent handle user interruptions?

Use the Barge-in Detection mechanism. Employ semantic VAD rather than relying only on silence detection, so that when the user begins speaking, the system can quickly recognize it and interrupt the current response. This requires coordination between the client and server to achieve interruption response times under 200 ms.