AI glossary Data & Retrieval
What is agentic retrieval-augmented generation (Agentic RAG)?
Agentic RAG (Agentic Retrieval-Augmented Generation)
← All glossary termsAn AI architecture that uses autonomous agents to iteratively search, evaluate, and synthesise information across multiple data sources until a complex question is fully answered.
Explained
Agentic RAG moves beyond the single-pass document fetching of standard retrieval-augmented generation. Instead of just pulling data once based on a user's prompt, it empowers an AI agent to break down a complex task, formulate its own search queries, and evaluate the retrieved context. If the initial information is incomplete, the agent autonomously adjusts its approach and searches again across different enterprise systems. This iterative process allows the AI to handle multi-step reasoning and connect fragmented data points.
How agentic RAG works
First, the AI analyses the user request and determines what data is needed to answer it. It then creates specific search queries for different databases, APIs, or document stores. After fetching the data, the agent assesses if it is sufficient to solve the problem. If information is missing, it refines its queries and searches again until the context is complete. Finally, the model compiles the gathered data into a comprehensive response.
Agentic RAG vs. standard RAG
Standard RAG is a linear process that takes a prompt, searches a vector database once, and generates an answer using whatever it finds. It works well for straightforward questions but fails when the answer requires synthesising information from multiple distinct sources. In contrast, agentic RAG is an active, multi-step workflow. The AI acts as a researcher, dynamically deciding which tools to use and when to keep digging, making it capable of solving complex enterprise workflows that require logical leaps and cross-referencing.
When it makes sense
Agentic RAG makes sense when a query cannot be answered by a single document or database. It is ideal for tasks that require connecting unstructured data, like PDFs, with structured data, such as a CRM database. This approach is also suited for workflows that demand high accuracy and the ability to verify facts before generating an answer. It is best applied when slower response times are acceptable in exchange for deep reasoning and comprehensive research.
Frequently asked questions
Why do we need agentic RAG?
Because simple RAG struggles with complex queries. When a task requires comparing data across different formats, departments, or timeframes, a single search pass rarely retrieves all the necessary context. Agentic RAG solves this by allowing the AI to keep searching until it has the full picture.
What is an agent in this context?
An agent is a large language model equipped with the ability to plan, make decisions, and use external tools. In agentic RAG, the agent uses search APIs, SQL databases, or vector search engines as tools to gather information autonomously.
Does agentic RAG hallucinate less?
It often reduces hallucinations because the model evaluates the retrieved context before generating a final answer. If it detects conflicting or missing information, it can run another search to verify the facts rather than guessing.
Is agentic RAG slower than standard RAG?
Yes. Because the agent performs multiple search operations, evaluates the results, and sometimes restarts the process, it takes longer to produce an answer. It trades immediate latency for accuracy and depth.
What tools are used to build agentic RAG?
Developers typically use orchestration frameworks like LlamaIndex. These frameworks help define the agent's behaviour, the tools it can access, and the memory structures it uses to track its progress during a task.
Related terms
Sources
Updated: 2026-10-26