AGENTIC-SYSTEMSJuly 28, 20261 min read

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

Researchers have introduced SCAIR, a training-free agentic framework that enhances Knowledge Graph-based RAG (KG-RAG) for enterprise applications by injecting schema constraints into multi-hop iterative reasoning.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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