Introduction to Agentic RAG: Building Smart Autonomous Data Workflows

First-generation RAG pipelines are completely passive. A user types a query, the backend executes a single database lookup, passes the results to the LLM, and hopes for the best. If the vector database pulls bad or irrelevant document chunks, the AI generates a bad answer.

Agentic RAG introduces a loop of autonomy. Instead of running a single linear script, the AI acts as an autonomous agent that can critically evaluate its own search results, decide if it needs more information, and iterate until it finds the absolute truth.