What is contextual retrieval? How AI agents find the right context

Most RAG systems today score how semantically similar a new query is to stored text chunks and return a list of the best matches. However, information doesn’t need to include similar words to be relevant. Data entities might be related in ways that similarity scoring can’t see. A model might be reasoning well, but if the retrieval mechanism lacks what it needs, the agent’s accuracy will suffer. Contextual retrieval works by following connections instead of matching words to find relevant context. In a graph-based retrieval system, vector search finds a starting point, and graph traversal expands from it to related facts, messages, and prior decisions. A context graph gives those connections a durable structure that an agent can query and update as work continues. Vector-only RAG vs. graph-based contextual retrieval A context graph stores context as persistent memory for AI agents. In a graph, the connections between data entities are explicit and traversable, allowing the agent to easily gather the full context. Context graphs…