Basic retrieval-augmented generation (RAG) follows a straightforward pattern. A user asks a question, the system finds relevant content in a knowledge base, and the model uses it to ground its answer. This works for simple lookups, but many real-world retrieval systems need more control over how and where they search.

Agentic RAG lets an agent rewrite the question and choose where and how to search. It may query a knowledge base or an account system, combine lexical, semantic, and graph search, fuse the resulting scores, rerank candidates, discard weak results, and try again. This can find evidence that a single semantic search would miss. It also adds more decision points that should be supported by evidence, and a confident answer may not reveal the retrieval path that led to it. 

“The opportunity comes with a responsibility: more decisions require a clear evidence trail.”

The opportunity comes with a responsibility: more decisions require a clear evidence trail. More control can improve coverage, but control alone cannot create trust. The system earns that trust by showing what it searched and why it accepted a source. It must also disclose what it couldn’t verify. Without that record, it can be harder to understand the basis for even a good answer.

A retrieval turn may look like a single operation in the application, but the agent is making a chain of choices. It interprets the user’s intent and creates a query. Then it chooses data sources, applies the required filters, and inspects the results.

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