From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI

Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand—producing decisions that are fluent but ungrounded and carrying no audit trail. We present LOM-action, which equips enterprise AI with event-driven ontology simulation: business events trigger scenario conditions encoded in the enterprise ontology (EO), which drive deterministic graph mutations in an isolated sandbox, evolving a working copy of the subgraph into the scenario-valid simulation graph; all decisions are derived exclusively from this evolved graph. The core pipeline is event to simulation to decision, realized through a dual-mode architecture—skill mode and reasoning mode. Every decision produces a fully traceable audit log. LOM-action achieves 93.82% accuracy and 98.74% tool-chain F1 against frontier baselines Doubao-1.8 and DeepSeek-V3.2, which reach only 24–36% F1 despite 80% accuracy—exposing the illusive accuracy phenomenon. The four-fold F1 advantage confirms that ontology-governed, event-driven simulation, not model scale, is the architectural prerequisite for trustworthy enterprise decision intelligence.