Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents

This paper proposes a neurosymbolic architecture for domain-grounded AI agents in enterprise systems, integrating ontology-constrained neural reasoning. The approach aims to enhance the trustworthiness and explainability of AI in regulated industries by combining the power of neural networks with symbolic knowledge representation. Empirical evaluations across various industries, including Vietnamese-language domains, demonstrate its potential for creating robust and context-aware AI agents that can operate reliably in complex business environments, ensuring adherence to domain-specific rules and regulations.