Neurosymbolic Architecture for Enterprise AI Agents Addresses LLM Limitations

A neurosymbolic architecture implemented within the Foundation AgenticOS platform addresses enterprise adoption challenges of Large Language Models. The approach introduces a three-layer ontological framework—Role, Domain, and Interaction ontologies—that provides formal semantic grounding for LLM-based enterprise agents. This architecture tackles limitations including hallucination, domain drift, and inability to enforce regulatory compliance at the reasoning level. The concept of asymmetric neurosymbolic coupling is formalized, where symbolic ontological knowledge constrains agent inputs such as context assembly, tool discovery, and governance thresholds. Mechanisms are proposed for extending this coupling to constrain agent outputs through response validation, reasoning verification, and compliance checking. The architecture was evaluated through a controlled experiment involving 600 runs across five unspecified scenarios. The research paper is identified as arXiv:2604.00555v2, indicating it's a replacement version of a previous submission. The work focuses specifically on enterprise agentic systems and their need for domain-grounded AI agents. No specific implementation timeline or commercial deployment details are provided in the abstract. The approach represents a technical solution to practical problems facing enterprise AI adoption.