如何构建客户支持语音智能体

Voice AI agent demo often looks simple:

语音AI智能体演示通常看起来很简单：

Caller → Speech-to-Text → LLM → Text-to-Speech → Caller

呼叫者 → 语音转文本 → LLM → 文本转语音 → 呼叫者

In production systems this looks very different.

在生产系统中，这看起来非常不同。

Between those components lies the real engineering involved in low-latency audio streaming, turn detection, interruption handling, telephony codecs, webhook orchestration, and voicemail detection.

在这些组件之间，蕴含着低延迟音频流、话轮检测、打断处理、电话编解码器、webhook 编排以及语音邮件检测所涉及的核心工程实现。

In this tutorial, we'll build a customer support voice agent without assembling that pipeline yourself. Using Telnyx AI Assistant Builder, you'll configure the entire voice stack from a portal and write a small FastAPI webhook that injects live business context into every call.

在本教程中，我们将构建一个客户支持语音代理，而无需自己拼装该流水线。使用 Telnyx AI 助手构建器，您将通过门户配置整个语音技术栈，并编写一个小型的 FastAPI webhook，将实时业务上下文注入到每通电话中。

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What a working customer support voice agent actually requires

一个真正可用的客户支持语音智能体实际需要什么

The same core building blocks, regardless of the tools you choose:

无论您选择何种工具，核心构建块都是相同的：

Sub-second round-trip latency. STT, the language model, TTS, and the telephony layer all have to fit inside about one second, or the conversation feels awkward.

亚秒级往返延迟。STT、语言模型、TTS 和电话层都必须在大约一秒内完成，否则对话会显得很不自然。
Grounded answers. The agent has to speak from a knowledge base, not from vibes. Hallucinated policies hurt more than they help.

有据可查的回答。智能体必须基于知识库作答，而非凭空臆想。虚构的政策弊大于利。
Live context per call. Queue wait, incidents in the last hour, whether the branch is open right now. None of that can live in a static prompt.

每次通话的实时上下文。队列等待时间、过去一小时的事件、分支机构当前是否营业。这些都无法存在于静态提示词中。
Graceful handoff. Some questions do not get answered by AI. The agent needs to know when to stop trying and pass the caller to a human, cleanly.

优雅交接。有些问题AI无法解答。智能体需要知道何时停止尝试，并干净利落地将呼叫者转接给真人。
Testable without a phone number. You should be able to iterate on t...