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The article discusses the risks of AI voice agents making promises to customers, such as scheduling callbacks or refunds, which could create unexpected work for other teams and highlight ethical concerns in automation.
The article highlights how aggregate Word Error Rate (WER) metric fails to capture critical errors in voice agents for production, such as misrecognized bank codes and multilingual speech, necessitating per-field tracking for real-world applications like banking and collections.
The article reflects on the need for stability over speed in ASR for voice agents, discussing the 'Confucius r2t2' model that integrates wait/commit mechanisms to enhance reliability.
NetEase Youdao open-sourced Confucius4-R2T2, a streaming ASR model for voice agents that incrementally processes speech and emits only committed text to prevent state corruption.
Google releases Gemini 3.8 Live and 3.8 Live Extended Thinking for production-grade voice agents, along with other AI news including regulatory disputes, safety concerns, and a Stanford-MIT paper on model harnesses.
An AI developer shares common debugging pitfalls when building voice agents and automation workflows, emphasizing practical strategies like logging errors and testing in real environments.
Google introduces Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, advanced AI models for real-time reasoning and voice agents, achieving top scores in various benchmarks.
The paper introduces τ-Elicitation, a benchmark for evaluating multi-turn entity extraction in voice agents, identifying strategy selection and error recovery as key bottlenecks.
A tweet from @alexandr_wang shares a user's notification that @Muse can now make phone calls using voice agents, questioning its current functionality and scheduling a call.
The article discusses key challenges in memory retrieval for voice agents, emphasizing the need for measuring P99 latency per turn, using prefetching, and budgeting memory tokens to reduce latency and improve user experience.
The paper introduces Duplex Cue, an evaluation framework for assessing in-turn adaptation in full-duplex voice agents, comparing human and model responses to overlapping speech.
OpenAI has launched GPT-Live-1, a full-duplex voice model for API that enables natural, bidirectional voice conversations for developers, reducing latency and improving turn-taking in voice agents.
The author shares a postmortem on building a production phone-based AI voice agent, revealing that most engineering time was consumed by telephony infrastructure, turn detection, observability, and failure handling rather than core LLM behavior. They suggest using managed platforms like Vapi, Retell, or Dasha from the start to focus engineering effort on business logic.
The article compares performance metrics of AI models like GLM-5.3 and GPT-5.5 on a benchmark, highlighting cost efficiency and questioning the benchmark's validity, while seeking efficient methods for methodology evaluation.
The paper introduces DuplexSpeechBench-IFEval, a benchmark for evaluating implicit instruction following in full-duplex voice agents, with 1,038 test cases across eight roles and five protocols to assess real-time speech systems' adherence to explicit vs. persona-implied behaviors.
An engineer at Hugging Face is recruiting for a role to build voice agents, inviting candidates familiar with their demos and code to apply.
The study tests real-time voice AI models on emotional cues in speech, finding that while they can detect emotions like distress or sarcasm, this information doesn't reliably influence decisions, highlighting an emotional intelligence gap in current systems.
Voice agents often face latency issues not mainly from the model but from components like endpointing; optimizing VAD and using proper benchmarking can reduce turn latency effectively.
ElevenLabs MCP in Claude allows users to create and manage voice agents within chat systems, integrating ElevenLabs' voice technology with Claude's AI capabilities.
LangChain is hosting a webinar on how to evaluate voice agents across execution, outcomes, and experience, covering tool use, task completion, latency, interruptions, and conversational friction.