SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

Researchers propose SCAIR (Schema-Conditioned Agentic Iterative Reasoning), a training-free framework that improves Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) for enterprise knowledge graphs by injecting schema-conditioned structural priors and enforcing schema-aware traversal during multi-hop reasoning. Experiments on a real-world Configuration Management DataBase (CMDB) show substantial performance gains over existing methods without costly model retraining.

arXiv:2607.22571v1 Announce Type: new Abstract: Knowledge Graph-based Retrieval-Augmented Generation KG-RAG enables natural language interaction with structured enterprise knowledge, yet existing agentic approaches that perform well on public benchmarks often fail to generalize to real-world enterprise Knowledge Graphs KGs , which are dense, schema-driven, and operationally constrained. To address these limitations, we propose SCAIR Schema-Conditioned Agentic Iterative Reasoning , a training-free framework that integrates structured planning with controlled iterative reasoning by injecting schema-conditioned structural priors and enforcing schema-aware traversal during multi-hop reasoning. Experiments on an enterprise-oriented benchmark constructed from a real-world Configuration Management DataBase CMDB demonstrate that SCAIR substantially improves performance over existing KG-RAG methods. Crucially, our study highlights that reliable enterprise graph reasoning cannot rely on generic agentic designs; instead, it must explicitly incorporate the target domain's structural and operational constraints into the reasoning process. We demonstrate that by aligning agent design with business logic, substantial performance gains can be achieved without the need for costly model retraining.