Retrieval that reasons, verifies, and acts
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.
How a single query flows
- 01
Plan the retrieval
An orchestrator decomposes user intent into sub-queries, chooses the right retriever (vector, BM25, graph, or SQL), and schedules them in parallel.
- 02
Retrieve and rerank
Cross-encoder reranking on top candidates lifts accuracy 10–20% over pure similarity search. Low-confidence candidates trigger a second retrieval hop automatically.
- 03
Verify and act
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.
What ships out of the box
Multi-hop retrieval
Follows links between documents and knowledge graph nodes to answer questions that span multiple sources, without bloating prompts with irrelevant context.
Cross-encoder reranking
A dedicated reranker scores every candidate against the original query, lifting precision where approximate answers aren't acceptable.
Citation-first outputs
Every claim is traceable to its source — clickable citations, span-level attribution, and confidence scores on every response.
Human-in-the-loop escalation
When confidence falls below threshold, the pipeline routes to a human with the full reasoning trace attached — not a bare question.