Blog

Why LiveKit agents stop for mm-hm, coughs and echo, how adaptive interruption and min_duration work in the source, and a test set with pass thresholds.

Jev model routing: use Jev as a model router that sends each voice turn to the right LLM. LangChain and API code, confidence gating, cost math, and evals.

Jev as a judge matched human labels on 500 of 500 decisions in LangChain's study, at $0.34 vs $28.17 for Claude. Then build and validate a Jev judge.

Jev Score, Noul and Choice explained: every voice agent metric as a real Jev question, with the JSON you send, the response, and Python to roll it up.

Verifiable rewards vs LLM judges vs Jev: one voice call scored three ways in code, with agreement, variance, and cost compared, and how to layer all three.

Jev use cases for voice agents: 25 runtime and QA examples, and how to use Jev: send one state with several typed questions and read calibrated answers.

Jev is a System One model, not an LLM. How a Jev agent works in voice AI: the no-LLM-in-the-loop stack, real API calls, the economics, and how to audit it.

Validate a voice agent before deployment with an 8-gate go/no-go checklist: accuracy, latency, task completion, failure handling, load, and compliance.

Evaluate voice variations with A/B tests in Deepgram: pin Aura-2 or Flux TTS per session, randomize by caller, size the sample, and avoid peeking errors.