How do AI voice agents work for customer service?

AI voice agents combine speech-to-text, natural language understanding, dialogue management, and text-to-speech to handle spoken customer interactions. The pipeline starts with speech recognition converting caller audio to text using models like Whisper or commercial ASR services. The NLU component identifies the caller intent, extracts relevant entities like account numbers and dates, and determines the sentiment. The dialogue manager uses an LLM to generate contextually appropriate responses, access customer records through tool calls, and execute actions like booking appointments or processing returns. Text-to-speech converts the response to natural-sounding speech using neural TTS models with appropriate prosody and emotion. The entire cycle happens in under 2 seconds for natural conversation flow. Advanced features include interruption handling where the agent stops speaking when the caller interrupts, multi-turn context management across complex conversations, and seamless handoff to human agents with full conversation context when needed.