Engineering voice agents: Latency, quality, and scale — Rishabh Bhargava, Together AI
TL;DR · AI Summary
Building high-quality, low-latency, scalable voice agents is now an engineering challenge requiring real-time response (<500ms), complex instruction handling, and tool calling — supported by Together AI’s infrastructure.
Key Takeaways
- Voice agents must respond under 500ms; delays beyond this cause user drop-off, m
- Complex workflows require tool calling and ambiguity resolution, not just base L
- Together AI offers AI-native cloud services for training/inference at scale, ser
Outline
Jump quickly between sections.
Rishabh Bhargava leads Voice AI at Together AI with a decade of AI infrastructure experience.
Billions of human-handled calls annually can be automated; voice is a natural human-computer interface.
Human conversations respond in ~300ms; AI exceeding 500ms causes noticeable lag, >1s leads to hang-ups.
Real-world tasks demand handling ambiguity and invoking tools to complete complex workflows.
Future voice agents will integrate multimodality, context memory, and dynamic toolchains for production use.
Mindmap
See how the topics connect at a glance.
查看大纲文本（无障碍 / 无 JS 友好）
- 语音代理工程挑战
- 实时性要求
- 响应<500ms
- 人类对话基准300ms
- 智能与功能
- 工具调用能力
- 处理模糊指令
- 基础设施支持
- Together AI云平台
- 服务Cursor等企业
Highlights
Key sentences worth saving and sharing.
When humans converse, we respond to each other’s cues in about 300 milliseconds; if an AI takes over 500ms, you’ll start noticing the delay.
Voice agents are no longer sci-fi or research—they’re primarily an engineering problem today, especially for rich, high-quality conversations.
Together AI provides reliable compute for model training and inference at scale, serving millions of developers and hundreds of companies.