Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
Summary: arXiv:2604.00555v1 Announce Type: new
Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Foundation AgenticOS (FAOS) platform that addresses these limitations through ontology-constrained neural reasoning.
Our approach introduces a three-layer ontological framework—Role, Domain, and Interaction ontologies—that provides formal semantic grounding for LLM-based enterprise agents. We formalize the concept of asymmetric neurosymbolic coupling, wherein symbolic ontological knowledge constrains agent inputs (context assembly, tool discovery, governance thresholds) while proposing mechanisms for extending this coupling to constrain agent outputs (response validation, reasoning verification, compliance checking).
Key Innovations and Findings
We evaluate the architecture through a controlled experiment involving 600 runs across five industries: FinTech, Insurance, Healthcare, Vietnamese Banking, and Vietnamese Insurance. The results indicate that ontology-coupled agents significantly outperform ungrounded agents on several metrics:
- Metric Accuracy: p < .001, W = .460
- Regulatory Compliance: p = .003, W = .318
- Role Consistency: p < .001, W = .614
These improvements were most pronounced in areas where LLM parametric knowledge was weakest, particularly in Vietnam-localized domains. Our contributions to the field include:
- A formal three-layer enterprise ontology model.
- A taxonomy of neurosymbolic coupling patterns.
- Ontology-constrained tool discovery via SQL-pushdown scoring.
- A proposed framework for output-side ontological validation.
- Empirical evidence for the inverse parametric knowledge effect, indicating that the value of ontological grounding is inversely proportional to LLM training data coverage of the domain.
- A production system serving 21 industry verticals with over 650 agents.
Conclusion
This research presents a significant advancement in the integration of ontology with neural reasoning, specifically within the context of enterprise AI agents. By addressing the critical issues of hallucination and regulatory compliance, our neurosymbolic architecture not only enhances the performance of LLMs but also aligns AI applications with industry-specific requirements. As organizations continue to navigate the complexities of AI deployment, our findings underscore the importance of semantic grounding and structured knowledge representation in achieving reliable and compliant AI systems.