What Is Agentic RAG? A Backend Developer's Intro
Short answer
Agentic RAG (Retrieval-Augmented Generation with agents) is a pattern where an LLM, instead of answering from memory alone, retrieves relevant data from your own sources (documents, databases, APIs) and can take actions across multiple steps to answer a question accurately.
Classic RAG vs Agentic RAG
- Classic RAG: retrieve → stuff context → generate. One pass.
- Agentic RAG: the model decides what to retrieve, may query multiple sources, and iterates until it has a confident answer.
Minimal Python shape
retriever = VectorStore(index="docs")
agent = Agent(model="llm", tools=[retriever.search])
answer = agent.run("Summarize our Q1 backend outages")FAQ
Why not just fine-tune the model?
Fine-tuning bakes in knowledge at training time; RAG keeps data fresh and citeable, which matters for changing business data.
Do I need a vector database?
For semantic search, yes — pgvector (PostgreSQL) is a great backend-dev-friendly option since you may already run Postgres.