Agentic RAG
Definition
Agentic RAG (also called Agent RAG or Agentic Retrieval) is an advanced RAG architecture where retrieval is not a single fixed step but a dynamic, multi-step process controlled by an AI agent. Unlike naive RAG (which retrieves once and generates), agentic RAG uses an LLM-powered agent that can: decide whether retrieval is needed, choose between multiple retrieval tools or knowledge sources, iteratively refine queries based on what was found, retrieve additional information if initial results are insufficient, and synthesize information from multiple retrieval steps into a coherent final answer. The agent operates in a ReAct (Reasoning + Acting) loop until it determines it has sufficient information.
Why It Matters
Agentic RAG is necessary for complex questions that cannot be answered from a single retrieval step. Multi-hop questions ('What are the integration capabilities of the plan that includes the feature X, and what is its pricing?') require retrieving information about the feature, then looking up the plan that includes it, then finding pricing for that plan — three separate retrieval steps that must be chained based on what was found. Simple RAG architectures cannot handle this; agentic RAG can. For AI chatbot applications handling complex, multi-part questions, agentic RAG dramatically improves answer completeness and accuracy.
How It Works
Agentic RAG is implemented using an LLM agent with retrieval tools. The agent receives the user's query and a set of tools (e.g., search_knowledge_base, lookup_pricing_table, get_account_information). The agent follows a ReAct loop: Thought (reason about what information is needed), Action (call a retrieval tool), Observation (process the retrieval result), repeat until sufficient information is gathered, then generate the final answer. Frameworks like LangChain Agents, LlamaIndex Agents, and OpenAI Assistants provide infrastructure for building agentic RAG systems. The agent's reliability depends on the quality of the retrieval tools and the agent's ability to reason about when to stop retrieving.
Agentic RAG — Iterative Reasoning Loop
Agent
LLM orchestrator
ReAct Loop
max 5 iterationsPlan
Decompose into sub-tasks
Execute
Retrieve, reason, act
Observe
Check result quality
No
Loop back
Yes
Continue
Final Answer
Synthesized from all retrieval steps
Real-World Example
A 99helpers customer serves enterprise clients with complex pricing structures. Simple questions like 'What is the Enterprise plan price?' use standard RAG. But complex questions like 'We have 500 users who need SSO and API access — what plan do we need and what will it cost with annual billing?' require multi-hop reasoning: retrieve feature requirements, look up plan comparison, find pricing for qualifying plans, then calculate annual billing discount. An agentic RAG architecture chains these retrievals automatically, generating a complete, accurate quote recommendation — something simple RAG cannot do.
Common Mistakes
- ✕Using agentic RAG for simple questions where single-step RAG suffices — agents add latency and complexity; reserve for genuinely multi-hop questions
- ✕Not setting maximum iteration limits — an agent without iteration limits can enter long reasoning loops, consuming time and API tokens before timing out
- ✕Insufficient tool descriptions — the agent selects tools based on their descriptions; vague or inaccurate descriptions cause the agent to use wrong tools or get stuck
Related Terms
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language model responses by first retrieving relevant documents from an external knowledge base and then using that retrieved content as context when generating an answer.
Query Rewriting
Query rewriting is a technique that transforms a user's original query into an improved version — clearer, more complete, or better suited for retrieval — using an LLM to improve recall and relevance before searching the knowledge base.
Multi-Query Retrieval
Multi-query retrieval generates multiple alternative phrasings of the user's question and retrieves documents for each phrasing separately, then merges results to achieve higher recall than any single query formulation would provide.
RAG Evaluation
RAG evaluation is the systematic measurement of a RAG system's quality across multiple dimensions — including retrieval accuracy, answer faithfulness, relevance, and completeness — to identify weaknesses and guide improvement.
Context Window
A context window is the maximum amount of text (measured in tokens) that a language model can process in a single inference call, determining how much retrieved content, conversation history, and instructions can be included in a RAG prompt.
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