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

In three linesNeurosymbolic architecture with ontologies (Role, Domain, Interaction) for enterprise LLM agents. Controlled experiment (1,800 runs, Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B): ontology-constrained agents outperform ungrounded agents on metric accuracy and role consistency (p < .001). 2x greater lift in localized domains (Vietnam) where LLM training coverage is weak.

Summary generated by Claude — human-verified