Table of Contents

What is Agentic RAG?


TL;DR

Agentic RAG is an advanced form of Retrieval-Augmented Generation (RAG) where an AI agent actively controls the retrieval process rather than following a fixed retrieve-then-generate pipeline. Instead of fetching documents once and passing them to the model, an agentic RAG system reasons about what information it needs, formulates targeted queries, evaluates the quality of retrieved results, and iteratively refines its search until it has sufficient context to produce an accurate answer. This transforms RAG from a static, single-pass technique into a dynamic, multi-step reasoning workflow.


What Exactly is Agentic RAG?

Traditional RAG follows a straightforward pipeline: take the user's question, retrieve the top-K most similar documents, inject them into the prompt, and generate a response. This works well for simple factual lookups but breaks down when:

  • The question is ambiguous and needs reformulation before retrieval can succeed
  • The answer spans multiple documents that must be found and synthesized separately
  • The initial retrieval returns irrelevant results and a different search strategy is needed
  • The question requires multi-hop reasoning, where findings from one retrieval inform the next query

Agentic RAG addresses these limitations by placing an AI agent in control of the entire retrieval loop. The agent uses planning, reasoning, and tool use to decide when to retrieve, what to search for, whether the results are sufficient, and when to stop searching and produce a final answer.

From Pipeline to Agent Loop

Aspect Traditional RAG Agentic RAG
Retrieval control Fixed: one query, one retrieval pass Dynamic: multiple queries, iterative refinement
Query formulation User's question used directly Agent reformulates queries based on reasoning
Result evaluation None; top-K always injected Agent assesses relevance and decides next action
Multi-hop support Limited; requires prompt chaining Native; agent chains retrievals as needed
Error recovery None; bad retrieval produces bad output Agent detects poor results and adjusts strategy
Tool integration Retrieval only Retrieval, web search, computation, and more

Why Agentic RAG Matters

  1. Higher Answer Quality: By iteratively retrieving and evaluating information, the agent converges on more accurate, complete answers than a single retrieval pass can produce.

  2. Complex Question Handling: Questions like "Compare the security policies of documents A and B" require retrieving from two separate sources and synthesizing. An agent naturally decomposes this into subtasks.

  3. Adaptive Retrieval Strategy: If vector similarity search returns poor results, the agent can switch strategies: reformulate the query with different keywords, apply reranking, or fall back to a web search tool.

  4. Self-Correcting Behavior: Through reflection, the agent can detect when retrieved context contradicts itself or is insufficient, triggering additional retrieval before generating a potentially hallucinated response.

  5. Reduced Hallucination: By grounding each reasoning step in retrieved evidence and verifying completeness, agentic RAG significantly reduces the risk of generating fabricated information. See also AI Agent Grounding.


Technical Insights

Core Patterns in Agentic RAG

1. Iterative Retrieval

The simplest agentic RAG pattern. The agent retrieves, evaluates, and retrieves again if needed:

User question: "What are the main risks in our Q3 financial report?"

Step 1 - Retrieve: Search knowledge base for "Q3 financial report risks"
Step 2 - Evaluate: Found general Q3 overview but no specific risk section
Step 3 - Refine: Search for "Q3 risk factors" and "Q3 audit findings"
Step 4 - Evaluate: Found detailed risk disclosures
Step 5 - Generate: Synthesize answer from accumulated context

2. Query Decomposition

The agent breaks a complex question into simpler sub-queries, retrieves for each, and merges results:

User question: "How does our refund policy compare to our main competitor?"

Decomposition:
  Sub-query 1: "What is our refund policy?" → Retrieve from internal docs
  Sub-query 2: "What is competitor X refund policy?" → Web search tool
  Synthesis: Compare both policies and generate answer

This pattern leverages the agent's planning capability to identify independent information needs.

3. Adaptive Retrieval with Tool Selection

The agent chooses the best retrieval source based on the question type:

User question: "What was Bitcoin's price when our crypto policy was published?"

Step 1 - Reasoning: This requires two types of information
Step 2 - Tool Selection:
  - Internal RAG → Search for "crypto policy publication date"
  - Web Search tool → Look up Bitcoin historical price for that date
Step 3 - Combine: Merge both pieces of information into answer

4. Self-Reflective RAG

The agent critically evaluates its own retrieval and generation quality:

Step 1 - Retrieve: Get initial context
Step 2 - Draft: Generate preliminary answer
Step 3 - Reflect: "Does my answer fully address the question?
                    Are there unsupported claims?"
Step 4 - If gaps found: Retrieve additional context for weak areas
Step 5 - Revise: Generate improved answer with complete evidence

This pattern combines retrieval with agent reflection for higher accuracy.

How Agentic RAG Builds on Foundational Concepts

Agentic RAG is not a standalone technique. It is the natural convergence of several capabilities:

  • RAG provides the retrieval and embedding infrastructure (RagEngine, DataSource, Partitions)
  • AI Agents provide the autonomous reasoning loop
  • Planning provides strategies like ReAct to interleave retrieval and reasoning
  • Tools provide retrieval as one capability among many (web search, calculation, file reading)
  • Memory lets the agent retain and reuse retrieved knowledge across turns
  • Reranking improves retrieval precision within each retrieval step

Practical Use Cases

  • Enterprise Knowledge Assistants: Answer complex questions that span multiple internal documents, policies, and databases by decomposing the query and retrieving from multiple sources.

  • Research Agents: Given a research question, iteratively search academic papers, extract key findings, follow citation chains, and synthesize a literature review. See the Research Assistant demo.

  • Customer Support: Resolve tickets by first searching the knowledge base, then checking order history via API tools, then searching for similar resolved tickets if the first retrieval was insufficient.

  • Legal and Compliance: Answer regulatory questions by searching across policy documents, case law databases, and regulatory updates, verifying that all relevant sources have been consulted before generating a response.

  • Document Q&A: Go beyond simple single-document chat by navigating across chapters, cross-referencing appendices, and following internal document references. See the Chat with PDF demo.


Key Terms

  • Agentic RAG: A retrieval-augmented generation approach where an AI agent autonomously controls the retrieval process through reasoning, planning, and iterative refinement.

  • Query Decomposition: Breaking a complex question into simpler sub-queries that can be independently retrieved and answered.

  • Iterative Retrieval: Performing multiple rounds of retrieval, where each round is informed by the results of previous rounds.

  • Self-Reflective RAG: A pattern where the agent evaluates the quality and completeness of its own retrieval before generating a final answer.

  • Adaptive Retrieval: Dynamically selecting the retrieval strategy or data source based on the nature of the query and intermediate results.

  • Multi-Hop Reasoning: Answering questions that require chaining together information from multiple retrieval steps, where each step depends on the previous one.

  • Retrieval Grounding: Ensuring every claim in the generated response is traceable back to a specific retrieved source.





External Resources


Summary

Agentic RAG represents the evolution of retrieval-augmented generation from a static pipeline into an intelligent, agent-driven workflow. By giving an AI agent control over the retrieval process, including query formulation, source selection, result evaluation, and iterative refinement, agentic RAG handles complex, multi-hop questions that defeat traditional single-pass retrieval. The agent's ability to reason about what information it needs, assess whether retrieved context is sufficient, and adapt its strategy on the fly produces more accurate, complete, and grounded responses. Built on the convergence of RAG, agent planning, tool use, and reflection, agentic RAG is the natural next step for any application that needs reliable answers from large knowledge bases.

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