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Beyond simple search. Agentic RAG combines multi-hop retrieval with reranking, verification, and tool execution so every answer is defensible and every action is auditable.
Traditional RAG returns the best-matching chunks and hopes a language model sorts it out. Agentic RAG wraps that step in a loop: it plans the retrieval, checks results against business rules, traverses related context when gaps are detected, and only then commits an answer or an action.
An orchestrator decomposes user intent into sub-queries, chooses the right retriever (vector, BM25, graph, or SQL), and schedules them in parallel.
A dedicated reranker scores every candidate against the original query, lifting precision where approximate answers aren't acceptable.
A verification chain checks citations, business rules, and policy constraints before a response returns. When allowed, the agent completes the action — updating a ticket, running an API call — instead of handing work back to a human.
Follows links between documents and knowledge graph nodes to answer questions that span multiple sources, without bloating prompts with irrelevant context.
A dedicated reranker scores every candidate against the original query, lifting precision where approximate answers aren't acceptable.
Every claim is traceable to its source — clickable citations, span-level attribution, and confidence scores on every response.
When confidence falls below threshold, the pipeline routes to a human with the full reasoning trace attached — not a bare question.