Research1d ago
LOM-Action Uses Graph Simulation For Auditable AI
The LOM-action framework forces AI agents to derive decisions from a deterministic simulation graph rather than unrestricted knowledge. Business events trigger specific ontology mutations in an isolated sandbox to ensure grounded outputs. This architecture creates a verifiable audit trail for enterprise decisions. Practitioners can now eliminate the fluency-without-grounding problem common in standard LLM agent workflows.