SCAIR Improves LLM Reasoning on Enterprise Knowledge Graphs

While Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, existing agentic approaches often struggle to generalize effectively to real-world enterprise Knowledge Graphs (KGs). These KGs are typically dense, heavily schema-driven, and subject to operational constraints, which generic agent designs fail to account for. To overcome these limitations, researchers propose SCAIR (Schema-Conditioned Agentic Iterative Reasoning). This training-free framework integrates structured planning with controlled iterative reasoning. It achieves this by injecting schema-conditioned structural priors and enforcing schema-aware traversal throughout the multi-hop reasoning process. This explicit incorporation of domain-specific structural and operational constraints is key to its success. Experiments conducted on an enterprise-oriented benchmark, built from a real-world Configuration Management DataBase (CMDB), demonstrate that SCAIR substantially outperforms existing KG-RAG methods. The study emphasizes that reliable enterprise graph reasoning necessitates aligning agent design with specific business logic, rather than relying on generic approaches. This alignment leads to significant performance gains without the need for costly model retraining.