Sam Charrington
Host of the TWIML AI Podcast and an AI industry analyst, speaker, and commentator focused on practical business and consumer applications of machine learning and AI.
Natural Voice Agents Must Balance Latency, Intelligence, and Cost
Boson AI co-founder and chief executive Alex Smola argues that voice agents will not become useful simply by producing accurate speech: they must respond, pause, handle interruptions and use tools at the pace and emotional register of human conversation. That makes latency, audio representation and inference cost product constraints rather than back-end engineering details, he says. As systems add vision and avatar-like presence, Smola contends, they will need to manage those trade-offs while learning when and how to adapt to individual users.
Production Analytics Finds Agent Failures That Standard Evals Miss
Scott Clark, co-founder and chief executive of Distributional, argues that teams running LLM agents need to look beyond pre-production evals and dashboards of known metrics. His case is that the most consequential failures often emerge only in production, where agents interact with users, tools and changing models in ways teams did not know to test. Clark proposes an observability stack in which telemetry records what happened, monitoring tracks known signals, and analytics clusters trace behavior to surface unknown failure modes that can become new evals, guardrails, prompts or system fixes.