Cascaded vs Fused Models: Voice Agent Architectures

Conversational agent architectures vary widely, existing on a spectrum between cascaded and fused models, each offering distinct advantages and tradeoffs in terms of reasoning, control, and naturalness. Cascaded architectures, like those used by ElevenLabs, break down processes into modular components such as speech recognition, reasoning, and speech generation, allowing for precise control and the ability to incorporate advanced language models for better reasoning. However, they often lose natural prosodic elements since speech is converted to text before being regenerated. Conversely, fused models, like OpenAI's Realtime approach, process audio end-to-end in a single network, preserving natural speech cues but offering less control and making testing difficult. Teams choose from five main architectures—basic cascaded, advanced cascaded, hybrid cascaded and fused, sequential fused, and duplex fused—based on their goals for reasoning, reliability, and prosody. Each architecture serves different use cases, from customer support and AI receptionists to language learning and social voice apps, with the choice depending on the desired balance between predictability and natural conversational flow.

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