Grounded manufacturing ontology slashes AI agent hallucinations 43% to 0%

Large language model-based AI agents are being deployed in manufacturing for analytics and decision support, but they suffer from a critical flaw: they know industry terminology but not the relational semantics that connect equipment IDs, process parameters, and regulatory constraints in a specific factory. A new arXiv preprint by Grama Chethan (submitted 11 May 2026) formalizes this as the 'semantic training gap' and shows it leads to operationally incorrect outputs even when responses sound precise. In multi-agent setups, the gap compounds into 'semantic drift' — a failure mode where errors cascade between agents.

The paper introduces an architecture that embeds manufacturing ontology directly into the AI tool layer as a typed relational configuration, enforcing semantic constraints at runtime rather than relying on model training. The architecture defines a three-operation interface contract (resolve, contextualize, annotate) with invariants enforced by an AIOps orchestration layer. In a controlled experiment across six industry configurations — 72 tool invocations using Qwen3-32B — unconstrained tool parameters produced a 43% hallucination rate for domain identifiers, while ontology-grounded parameters reduced this to 0%. The approach was validated with a digital twin analytics platform, showing that a single codebase with domain-specific ontology configurations eliminates tool-call hallucination and enables cross-domain configurability without application code changes.