AG-RAG: Robust Agentic Graph RAG through Stress-Aware Self-Improvement

Graph retrieval-augmented generation (Graph RAG) organizes external knowledge into entities, relations, and supporting documents to improve LLM reasoning. However, existing Graph RAG systems are typically optimized and evaluated on clean queries, where their advantages over text retrieval can be modest. We argue that graph structure can be especially valuable when retrieval fails. We introduce AG-RAG, an agentic Graph RAG framework designed for such failures. AG-RAG exposes a corpus-derived knowledge graph through MCP tools for fuzzy entity resolution, graph traversal, and provenance-bound document retrieval, together with stress-aware self-improvement that optimizes tool policies, retrieval budgets, ranking, and instructions while rejecting changes that sacrifice robustness. We evaluate three retrieval failures: misspelled entities, unavailable source text, and adversarially injected passages. With the same answering model, AG-RAG remains competitive with HippoRAG 2 and LinearRAG on clean HotpotQA, 2WikiMultiHopQA, and MuSiQue, while substantially outperforming both under stress. On 2Wiki-Extra, a set of 2WikiMultiHopQA questions never used during development, AG-RAG achieves only 2.2% poisoning attack success, compared with 57.2% for HippoRAG 2 and 48.0% for LinearRAG. These results show that graph structure provides complementary robustness against retrieval failures and suggest that self-improving retrieval agents should optimize capability subject to explicit stress-aware constraints.