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Author: search.filters.author.Koseeyaumporn, SawitSubject: search.filters.subject.Retrieval-Augmented Generation

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    Item type:Publication,
    MANJU: A Multi-Agent Framework for Natural Just-in-Time Understanding in Thai
    (2026-01-01)
    Koseeyaumporn, Sawit
    ;
    Saiprom, Siratee
    ;
    Promfiy, Watin
    ;
    Tarnpradab, Sansiri
    This paper presents MANJU (Multi-Agent AI for Natural Just-in-Time Understanding), an automated platform for Thai-language customer service delivered through just-in-time voice interaction. MANJU transcribes a customer's spoken query, applies multi-agent reasoning with retrieval-augmented generation (RAG) to determine the optimal response strategy, and synthesises a reply in natural Thai, delivering the first audio segment within approximately 1.8 s via sentence-level streaming. A visual drag-and-drop work flow editor enables non-technical operators to compose and modify conversation logic without programming.Evaluated on 50 standard Thai customer-service queries and 23 adversarial edge cases, MANJU achieves 100% intent routing accuracy, 62% RAG-grounded responses (GR), 78% edge-case acceptability with 100% safety, and Mean Opinion Score (MOS) = 4.33 for VoiceDesign TTS. End-to-end median latency is 6.07 s; the primary bottleneck is Qwen3-TTS at Real-Time Factor (RTF) = 1.54, partially offset by sentence-level streaming.