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All digests tagged Conversational AI
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Building human-like voice agents requires focusing on the entire conversational flow, not just individual model components. Key strategies include measuring time to first audio, using async tool calls, and benchmarking the full stack with simulated callers.
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Learn about the next generation of voice AI, covering native audio models, conversational latency, and agentic tasks. We discuss moving beyond WER to measure real-world dialogue quality.
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The agentic web is a shift, not a web of agents. We must break interfaces into atoms and let an assistant compose them, moving beyond the need for traditional browser tabs.
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Improving voice AI requires optimizing system behavior under uncertainty, not just accuracy. By minimizing the Outcome User Cost Heuristic (OUCH), developers can significantly boost UX even if the core recognition accuracy remains constant.
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Voice agents are scaling fast, but reliability is the number one problem. The speaker outlines a continuous monitoring loop, emphasizing that centralized AI systems require deep adversarial testing to prevent massive failures.
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Building smarter voice agents requires managing complex challenges like subsecond latency, multi-party turn-taking, and user trust. The discussion contrasts structured healthcare flows with open networking interactions.
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Building reliable voice agents requires complex cascaded pipelines (STT $\rightarrow$ LLM $\rightarrow$ TTS). Engineers must manage latency trade-offs, context limits, and model fallbacks to achieve human-like performance.
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Building a safe AI coach for mental health requires more than just LLMs; it demands an eval-driven playbook. SonderMind details its modular, clinically grounded architecture and how they integrate human expert judgment directly into their CI pipeline.
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