An Auditable Symbolic-RAG-Generative AI Architecture for Goal-Oriented Conversation Orchestration
Goal-oriented conversational systems must answer factual questions, understand visitor-provided information, and advance business objectives without becoming rigid questionnaires. This paper proposes a Symbolic-RAG-Generative architecture centered on the Goal-oriented Retrieval-Augmented Conversation Engine (GRACE). An instruction-constrained Business Goal Compiler transforms business intent into an immutable objective set, normalized priority vector, canonical questions, and initial state vector. At runtime, GRACE receives the complete conversation history, latest visitor message, current state, and grounded answer generated by a separate RAG component. It updates completion only from vi...
| Authors | Ramon Gonzalez, Antonio Diaz |
|---|---|
| Primary category | cs.AI |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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