AI Voice Agents Struggle to Handle Real-World Human Interruptions Effectively

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AI voice agents perform well in controlled demos but fall short in real-world conversations, particularly when users interrupt naturally. Key technical components — including speech recognition, interrupt detection, and context management — each introduce vulnerabilities that compound into poor user experiences. Common failures include false positive interruption triggers, response inertia where the agent keeps talking despite being cut off, and context bleed from limited conversation memory. These issues stem partly from the strict sub-150ms latency requirement needed to maintain natural turn-taking, leaving little margin for error. Collectively, these shortcomings pose a significant barrier to broader enterprise adoption of AI-driven customer service systems.

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