Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
/ quick answer
Agentic RAG combines retrieval with agent loops. The model plans sub-queries, picks data sources (vector store, SQL, web), critiques results, and re-queries until it has enough evidence to answer. It handles ambiguous questions that classic single-shot RAG fails on. RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
/ frequently asked
What is Agentic RAG?
Agentic RAG combines retrieval with agent loops. The model plans sub-queries, picks data sources (vector store, SQL, web), critiques results, and re-queries until it has enough evidence to answer. It handles ambiguous questions that classic single-shot RAG fails on.
What is an example of Agentic RAG?
A finance assistant gets 'why did revenue drop in Q2?', runs a SQL query for raw numbers, retrieves the relevant board notes from a vector store, then pulls a competitor news article from the web before composing the answer.
Why does Agentic RAG matter?
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
What should I learn after Agentic RAG?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Agentic Workflow
A workflow where an LLM decides the next step instead of a hard-coded path.
- →Retrieval
Selecting the most relevant chunks for a query before generation.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
- →MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
Related workflows
Turn this into a repeatable process.
- →RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
- →Customer Feedback Intelligence System
Turn support tickets, calls, reviews, and surveys into product signals.
- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →Knowledge Graph Stack
Relationship layer that maps concepts, workflows, prompts, tools, and cases.
Related prompts
Reusable prompts for this job.
- →Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
Comparisons & alternatives
Pick between the options.
- →OpenAI vs Anthropic for Agents
Which provider builds better production agents in 2026.
- →CrewAI vs AutoGen vs LangGraph
Three approaches to multi-agent orchestration.
- →Cursor Agents vs Devin vs Lovable
Three ways AI writes code for you in 2026.