Agentic GraphRAG: How does an AI agent use a knowledge graph?

Agentic GraphRAG extends graph-based retrieval by allowing an AI agent to dynamically decide how to search, traverse, and update a knowledge graph rather than following a fixed retrieval path. Using iterative reasoning loops, agents can combine vector search with multi-step graph queries to answer complex questions, such as identifying customers affected by a shared issue, while preserving an explainable record of their retrieval decisions. Agents may also extract new entities and relationships from incoming information, resolve duplicates or conflicts, and update the graph under controlled permissions, validation rules, or human review. A context graph further incorporates conversations, tool calls, decisions, and outcomes, enabling agents to reuse prior operational experience alongside domain facts. The approach is most valuable when questions require unpredictable retrieval paths, a domain evolves with new concepts and relationships, or past decisions must guide future actions, while simpler fixed GraphRAG patterns may remain faster for predictable queries.

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