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From Voice Agents to AI Avatars

The TWIML AI Podcast with Sam Charrington17 September 2026Watch on YouTube

What you'll learn

  • Voice AI has improved a lot, but natural conversation remains a high bar because small delays, awkward interruptions or the wrong tone break the illusion.
  • Alex Smola of Boson AI discusses the technical tradeoffs behind real-time voice, including audio tokenization, latency, model size and inference cost.
  • Adding vision and visual presence raises the stakes for audiovisual agents and AI avatars.
  • Emotional intelligence in AI plays a role in natural interaction, along with learning and personalization from human interactions.
  • The episode covers what it takes to move from impressive demos toward interactions that actually feel natural.

Frequently asked questions

Which technical tradeoffs are discussed for real-time voice agents?
The episode discusses audio tokenization, latency, model size and inference cost as the main technical tradeoffs behind real-time voice.
What changes when agents can both see and be seen?
According to the episode, adding vision and visual presence raises the stakes for audiovisual agents and AI avatars.
Why does natural conversation with voice AI remain so difficult?
Small delays, awkward interruptions or the wrong tone can quickly break the illusion of a natural conversation.
What role does emotional intelligence play in the episode?
Emotional intelligence is discussed as part of natural interaction, along with how agents can learn from human interactions.

Topics

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Description from the channel

Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion, and adding vision and visual presence only raises the stakes. In this episode, Alex Smola, co-founder and CEO of Boson AI, explores the path from today’s voice agents to audiovisual agents and AI avatars. We discuss the technical tradeoffs behind real-time voice, including audio tokenization, latency, model size, and inference cost, as well as what changes when these systems can both see and be seen. We also explore the role of emotional intelligence in AI, how agents can learn from human interactions, and what it will take to move beyond impressive demos toward interactions that actually feel natural. Full show notes including references: https://twimlai.com/go/777. 🔔 Subscribe to our channel for more great content just like this: https://youtube.com/twimlai?sub_confirmation=1 📖 CHAPTERS =============================== 00:00 Intro 01:11 The state of voice AI 06:10 Designing real-time voice systems 13:53 Boson AI’s path to audio 19:59 Building the audio data flywheel 28:41 Extending language models with audio 33:14 Architectures for responsive voice agents 39:26 Tools latency and natural interaction 46:20 Measuring interruption and proactivity 49:35 Emotional intelligence in AI 53:43 Learning and personalization from human interaction 01:03:56 Outro 🗣️ CONNECT WITH US! =============================== Subscribe to the TWIML AI Podcast: https://twimlai.com/podcast/twimlai/ Follow us on Twitter: https://twitter.com/twimlai Follow us on LinkedIn: https://www.linkedin.com/company/twimlai/ Join our Slack Community: https://twimlai.com/community/ Subscribe to our newsletter: https://twimlai.com/newsletter/ Want to get in touch? Send us a message: https://twimlai.com/contact/