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.
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 for AI and automation?
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ 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.