{"podcast":{"title":"MLOps.community","slug":"mlops-community","podcast_index_feed_id":28679,"rss_url":"https://anchor.fm/s/174cb1b8/podcast/rss","website_url":"https://mlops.community","image_url":"https://d3t3ozftmdmh3i.cloudfront.net/production/podcast_uploaded_nologo/3809022/3809022-1612190855115-e91f8b881173f.jpg","author":"Demetrios","episode_count":516,"summary":"Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)","last_synced_at":null,"page_url":"https://stenobird.com/podcast/mlops-community"},"episode":{"title":"Voice Agent Use Cases","slug":"voice-agent-use-cases","published_at":"2026-05-01T17:00:00+00:00","page_url":"https://stenobird.com/podcast/mlops-community/voice-agent-use-cases","show_page_url":"https://stenobird.com/podcast/mlops-community","url":"https://podcasters.spotify.com/pod/show/mlops/episodes/Voice-Agent-Use-Cases-e3ileg6","audio_url":"https://anchor.fm/s/174cb1b8/podcast/play/119240646/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2026-3-29%2F423136952-44100-2-d67b1521393a6.mp3","summary":"This episode is brought to you by the MLflow team. Check out more information at MLflow.org . What does it actually take to build voice AI at a billion-interaction scale? This episode features an ex-Amazon voice AI engineer who built customer support systems handling 2 billion+ interactions — now working on next-gen voice agent platforms. Anurag digs deep into the real engineering tradeoffs, design patterns, and use cases that separate production-grade voice agents from demos. Voice Agent Use Cases // MLOps Podcast #372 with Anurag Beniwal, Member of the Technical Staff at ElevenLabs 🎙️ Topics covered: 🔹 Cascaded vs. speech-to-speech — Why cascaded systems still win in production, and how to make them feel natural without sacrificing control 🔹 Latency masking — Foreground/background model architecture and how to buy yourself time while deep retrieval runs 🔹 Constellation of models — Using Haiku for tool calling, fine-tuned smaller models for response generation, and why "one model for everything" breaks at scale 🔹 Turn-taking & ASR challenges — Why voice is harder than chat: accents, noise, silence detection, and domain-specific fine-tuning 🔹 Level 1 vs Level 2 customer support — Why today's agents max out at Level 1 and what it takes to capture Level 2 expert judgment 🔹 Inbound vs. outbound sales agents — Where voice agents are already winning, and why inbound lead qualification beats cold outbound 🔹 Booking, reservations & concierge — The clearest near-term wins for voice agents across hospitality, home services, and SMBs 🔹 Continual learning from natural language feedback — How to build agents that improve from real operator feedback without ML expertise 🔹 Conversational TTS — Why passing full conversation history to your TTS model changes everythi…","meta_description":"This episode is brought to you by the MLflow team. Check out more information at MLflow.org . What does it actually take to build voice AI at a billion-in…","key_points":[],"chapters":[],"topics":[],"duration_seconds":3064,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/mlops-community/episodes/voice-agent-use-cases/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/mlops-community/voice-agent-use-cases.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}