An Oral-first Interactive Agentic System for Guaraní Speakers

Abstract

Artificial intelligence systems are often presented as universal, yet their interaction paradigms remain predominantly text-first, limiting alignment with primarily oral languages and communicative practices. Using Guaraní, an official and widely spoken language of Paraguay, as a motivating case, this work examines how language support risks remaining symbolic when spoken interaction is reduced to a speech-to-text interface. We explore an oral-first, multi-agent framing in which turn-taking, repair, shared context, and governance are treated as core components of interaction rather than peripheral features. By separating language understanding from the conversation state and permission mechanisms, the architecture makes conversational structure and control explicit, enabling reasoning over interaction dynamics rather than isolated commands. Framing conversational coordination as a cognitively motivated reasoning problem over shared state connects insights from human dialogue to the design of AI systems that are more interpretable and responsive in oral and low-resource settings.

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