Semantic Grounding as a Hallucination Mitigation Layer for Reliable AI Agents

AI agents remain unreliable in practice because hallucinations arise when autonomous systems reason over weakly grounded structured data. We show that semantic grounding of enterprise schemas using small language models, combined with uncertainty-aware human validation, constrains agent perception, reduces overconfident actions on ambiguous inputs, and acts as an effective hallucination mitigation layer for safe autonomous operation. Our results suggest that improving reliability in enterprise agentic systems requires controlling perception and uncertainty before planning, rather than scaling generation models alone.