Standard RAG retrieves documents based on static similarity search. Agentic RAG introduces autonomous reasoning loops where AI agents decide when to retrieve, evaluate retrieved facts, re-query, and call external tools to synthesize accurate responses.
Core Architecture of Agentic RAG
- Intent Decomposition: Breaking complex user prompts into discrete sub-questions.
- Dynamic Retrieval Strategy: Switching between vector databases, keyword search, and web search APIs dynamically.
- Fact Verification & Self-Reflection: Evaluating retrieved context for accuracy and hallucination detection before output generation.
Real-World Implementation Examples
From complex legal document analysis to multi-repository code investigation, Agentic RAG enables AI systems to operate with near-human accuracy on enterprise datasets.
