DeepLearning.AI and LiveKit ship a production voice-agent course
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Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with @livekit and @realavatarai, and taught by @dsa (Co-founder & CEO of LiveKit), @shayneparlo (Developer Advocate, LiveKit), and @nedteneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between https://t.co/zpIxRSuky4 and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and…
Context
A responsive voice needs to manage the timing of conversation as well as generate answers. The Building AI Voice Agents for Production announcement describes a modular pipeline that transcribes speech, generates a response with a , and converts that response into audio. Each stage can add delay.
The course pairs measurements of those delays with detecting when a person finishes speaking and handling interruptions. That gives developers separate problems to diagnose: slow processing and poorly timed conversational turns can both make an agent feel unresponsive. Its syllabus also compares this modular arrangement with direct speech-to-speech systems.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
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