Smarter retrieval with autonomous, tool-using AI agents
Agentic RAG upgrades static RAG by embedding LLM-powered agents that decide how to index data, route queries across tools, and critique answers before delivery. Using n8n’s visual workflows, it operationalizes intelligent storage, dynamic retrieval, and verified generation to boost accuracy and reliability. The result is an adaptive system that maintains its own knowledge and selects the right data source—vector store, SQL, or live web—on demand.
Points clés
- The article defines Agentic RAG as an autonomous upgrade to standard RAG, adding decision-making agents across indexing, retrieval, and generation.
- It addresses core LLM issues—hallucinations, knowledge cut-offs, and inconsistent outputs—by fetching context and validating answers.
- Intelligent storage: agents parse documents, enrich metadata, choose chunking, and select embedding models to optimize the knowledge base.
- Dynamic retrieval uses a “Retriever Router” to pick the best tool (e.g., SQL databases, vector stores, or web APIs) per query.
- Verified generation employs an Answer Critic to check completeness and correctness, looping retrieval when needed.
- Use case 1 (Adaptive RAG): an n8n workflow with Google Gemini and Qdrant classifies queries (Factual, Analytical, Opinion, Contextual), rewrites them, then retrieves and answers with tailored prompts.
- Use case 2 (Dynamic knowledge source): an AI Agent selects between a RAG MCP server for foundational info and a Search Engine MCP for real-time web answers via Model Context Protocol.
- Use case 3 (SQL + GraphRAG): tabular files are ingested into PostgreSQL/Supabase for precise querying, while PDFs/Docs feed LightRAG to build a knowledge graph stored in a graph database.
- Clarifications: Self-RAG embeds decisions inside the model; Agentic RAG orchestrates decisions in the surrounding workflow; Graph RAG focuses on knowledge graphs and can be one tool within an agentic system.
- Technologies and entities cited include n8n, Google Gemini, Qdrant, Supabase, PostgreSQL, LightRAG, Neo4j, ArangoDB, Pinecone, Milvus, and MCP; published by Mihai Farcas on September 3, 2025 (11 minutes read).
À retenir
Start simple: let an agent choose between your vector store and a web search, then add an Answer Critic to catch “creative” LLM moments. Keep tables in SQL (not in 2,000-token “chunks”), and use GraphRAG when relationships matter. Wrap it in n8n so you can see the logic flow—and iterate; or keep trusting a single index and hope for the best… what could possibly go wrong?
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