Home > Architecture > Agentic RAG

Agentic RAG Systems: Self-Correction, Rewriting & Re-Ranking

Architecture · 10 min read · Updated July 2026

Standard RAG pipelines fail when vector search returns irrelevant or incomplete documents. Agentic RAG introduces reasoning loops into the retrieval process: evaluating retrieved chunks, rewriting bad queries, and re-ranking documents dynamically.

1. The Self-Corrective RAG Loop

  1. Query Transformation: The agent reformulates ambiguous user questions into multiple targeted search queries.
  2. Document Grading: A fast LLM (like Claude Haiku 4.8 or Gemini 3.1 Flash) grades each retrieved chunk for relevance. Irrelevant chunks are discarded.
  3. Fallback Web Search / Router: If vector search fails to yield high-confidence documents, the agent dynamically invokes web search tools or alternative data stores.

2. Cross-Encoder Re-Ranking

Bi-encoder vector embeddings offer fast retrieval speed but lower semantic accuracy. Passing top-20 candidates through a Cross-Encoder re-ranker (such as `Cohere ReRank v3` or `bge-reranker-large`) ensures only top-3 relevant context chunks enter the final generation prompt.