How Voice Agents Handle Latency, Turn-Taking, and Safety

youtube-cover
Guest
Kræn Hansen
Software Engineer
@
MongoDB

Voice agents sound simple when you explain the pipeline: speech comes in, text gets processed, and speech comes back out. But how smooth is the  actual conversation?

In this interview, Luke Farrell talks with Kræn Hansen (ElevenLabs) about the problems users notice first when talking to voice agents. Latency matters, especially perceived latency: if users hear silence, they assume the connection dropped. If they hear cues that something is happening, the interaction feels intact.

The conversation gets into turn-taking, interruption handling, and audio feedback. It covers how voice agents can use spoken cues, background sounds, and better intent detection to avoid awkward cutoffs. It also gets into the tradeoff between speed, expressiveness, and safety when the agent is used in more sensitive settings.

The interview also covers implementation. Kræn walks through a layered SDK approach: a universal JavaScript core, React abstractions on top, and a React Native package for platform-specific audio input.

Insights

Learn more about AI

cover

Nov 17Talk

Efficient On-Device LLMs in React Native Done Right

Artur Morys-Magiera will cover efficient on-device LLMs in React Native, including runtimes, model selection, quantization, and platform constraints.

cover

Nov 17Talk

On-Device AI After WWDC: What Changed for React Native

Mike Grabowski will explain what changed for system-provided on-device AI after WWDC and what React Native developers can build with it.

cover

Nov 16Talk

Beyond Coding Agents: Making ADLC Work Across Platforms

Mike Grabowski will explain how to close the full agentic development lifecycle across mobile, web, and native platforms.

Building the React Native Software Factory — cover

Nov 11Talk

Building the React Native Software Factory

Join us at React Foundation Meetup London on November 11 to explore how AI agents build and debug React Native apps, from one terminal to the cloud.

Building a Software Factory: Write Specs an Agent Can Build From — cover

Nov 6Article

Building a Software Factory: Write Specs an Agent Can Build From

Learn how to research requirements, resolve open decisions, and write testable specs that AI coding agents can implement without the planning conversation.

 Large-Scale Migrations With Coding Agents: Planning, Verification, and Rollback — cover

Nov 4Article

Large-Scale Migrations With Coding Agents: Planning, Verification, and Rollback

Plan codebase migrations with coding agents using reviewable requirements, verification checks, staged rollouts, and defined rollback criteria.

Quantization Impact on LLM Quality: A Case Study of Apex — cover

Nov 4Talk

Quantization Impact on LLM Quality: A Case Study of Apex

Artur Morys-Magiera and Lech Kalinowski share findings from 344 benchmark runs with Apex, our Gemma-based LLM, examining the trade-offs between model size, decode speed, and quality.

Conversational Banking: From Customer Questions to Authorized Actions — cover

Nov 2Article

Conversational Banking: From Customer Questions to Authorized Actions

Explore conversational banking architecture through a card-dispute example, covering tool access, authorization, execution, observability, and human handoff.

Building a Software Factory: The Night My Agents Waited for Me — cover

Oct 28Article

Building a Software Factory: The Night My Agents Waited for Me

Learn how permissions, review limits, and missing product decisions stalled two overnight coding agent runs, and how their maintainer revised the workflow.