Oct 26, 2026

How to Build Voice Agents That Don't Fall Apart When You Interrupt - Jesse Hall, LiveKit

Jesse Hall of LiveKit explains how to build voice agents for financial services that feel human rather than robotic. The talk covers why model leaderboards mislead for real-time conversations, why time to first audio and time to first sentence matter more than first byte or first token, and how async tool calls keep a conversation flowing during slow back-end work. It also covers audio-aware turn detection, the roughly 1.5-second latency budget, and benchmarking your own stack with simulated callers.

Oct 11, 2026

Can Your Agent Hear You Now? Building Live Voice Agents with Gemini — Thor Schaeff

A live tour of Gemini's real-time and generative media stack: stateful image chaining with the Interactions API, near real-time speech translation with Gemini 3.5 Live Translate, a tool-calling AI radio DJ powered by Lyria 3, and a deep look at how the native-audio Live API works over WebSockets, including its 90-language support, barge-in and grounding.

Oct 01, 2026

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

Shawn, CTO of PolyAI, explains why voice agents are harder than text agents: voice adds time, and good conversation depends on adapting to the person on the line. He describes Dialog-RSN-1, an audio-native model that predicts turn-taking, replies in text with citations, and writes the transcript last for auditability, along with training on real noisy calls, latency and what agents should do while thinking, voice personality, benchmarking, harness ownership, and the shifting bottleneck from producing content to validating it.

Sep 22, 2026

100x More Reliable Voice Agents

SLNG.ai introduces Unmute, an open declarative standard for voice agents, addressing platform lock-in and the limitations of current agent architectures. It proposes a shift from LLM-centric, sequential pipelines to declared execution with structural guarantees, enabling portable, cost-effective, and robust voice agents by breaking down complex interactions into modular, context-aware steps. This approach minimizes token usage, enhances determinism, and improves agent reliability compared to traditional single-prompt designs.

Sep 18, 2026

Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon

Stefano Ermon, co-founder and CEO of Inception, discusses his company's radical bet on diffusion models to overcome the hardware and latency bottlenecks of autoregressive LLMs. He explains how parallel token generation in diffusion offers superior inference scaling and GPU utilization, expanding beyond images to discrete text and code generation. Ermon shares insights into Inception’s Mercury models, real-world applications like voice agents, the custom software stack required, and why efficiency will define the next era of AI competition.

Sep 15, 2026

Realtime Voice Agents with Frontier Intelligence — Bohan Li, EliseAI

Bohan Li of EliseAI unveils a sophisticated architecture for real-time voice agents that merges high intelligence with low latency, akin to a self-driving car's perception, planning, and control systems. Key innovations include a speculative transcriber combining fast streaming with accurate contextual correction, background agents for offloading LLM tool calls and enabling eager generation, and a clever prefix cache for speech synthesis that seamlessly blends pre-cached audio with dynamically generated speech to mask underlying processing delays.