Agentic RAG

Moving from static RAG pipelines to agent-driven retrieval. The agent decides what to retrieve, when, from which source, and evaluates retrieval quality with self-reflection.

Quick Reference

→Agentic RAG: an LLM agent controls the retrieval process instead of a fixed pipeline
→Agent decides: which retriever to use, what query to send, whether results are good enough
→Multi-source routing: different tools for different query types (docs, API, database)
→Self-reflection: agent checks retrieval quality and retries with a different strategy if needed
→LangGraph is the natural framework — model retrieval decisions as graph state transitions

From Static Pipeline to Agent-Driven Retrieval

Standard RAG is a fixed pipeline: embed query → search vector store → generate answer. The pipeline makes the same decisions for every query. Agentic RAG replaces this fixed pipeline with an LLM agent that dynamically decides how to retrieve information. The agent can choose between multiple retrievers, reformulate queries, evaluate results, and retry with different strategies. This flexibility dramatically improves answer quality for complex or ambiguous queries.

Aspect	Static RAG Pipeline	Agentic RAG
Retrieval strategy	Fixed (same for every query)	Dynamic (agent chooses per query)
Data sources	One vector store	Multiple sources (vector store, SQL, API, web)
Query handling	Direct embedding lookup	Agent reformulates, decomposes, or routes
Quality control	None — returns whatever the retriever finds	Agent evaluates results, retries if poor
Complexity	Simple, predictable	More complex, higher quality for hard queries
Latency	200-500ms	1-5s (multiple agent steps)

When to upgrade to agentic RAG

Upgrade from static RAG when: (1) you have multiple data sources (docs, databases, APIs), (2) your queries vary widely in complexity, (3) retrieval failures are common and reformulation would help, (4) users expect the system to try different approaches when the first doesn't work. Keep static RAG for simple, homogeneous knowledge bases.

Multi-Source Routing

In most organizations, knowledge lives in many systems: documentation in a vector store, structured data in SQL databases, real-time data in APIs, and policies in a separate document management system. An agentic RAG system gives the agent access to all of these as tools, and the agent decides which source(s) to query based on the question.

Self-Reflection: Quality-Aware Retrieval

The most powerful agentic RAG capability is self-reflection — the agent evaluates whether the retrieved context is sufficient and relevant before generating an answer. If the retrieval quality is poor, the agent can reformulate the query, try a different source, or expand the search. This mimics how a human researcher would assess their sources before writing an answer.

Sign in to read this article

This is a premium article. Sign in with your Google account to continue.