Agentic RAG: How AI Agents Are Transforming Retrieval-Augmented Generation

Name

Soren Atelier

Updated on 2/10/2026

Standard RAG (Retrieval-Augmented Generation) has a fundamental limitation: it retrieves once and generates once. The system searches a vector database, grabs the top-K chunks, stuffs them into a prompt, and hopes the answer is in those chunks. When the relevant information is spread across multiple documents, requires multi-step reasoning, or needs clarification before retrieval, basic RAG fails silently -- returning confident-sounding answers built on incomplete context.

Agentic RAG solves this by replacing the static retrieve-then-generate pipeline with an autonomous AI agent that can plan, retrieve iteratively, evaluate results, and decide when it has enough information to answer. The agent does not just fetch documents -- it reasons about what to search for, judges whether the retrieved content is sufficient, and takes additional actions when it is not.

Basic RAG vs Agentic RAG

Basic RAG Pipeline

User Query → Embed Query → Vector Search → Top-K Chunks → LLM → Answer

Problems with this approach:

• Single retrieval: If the first search misses relevant documents, there is no recovery
• No reasoning about what to retrieve: The query is embedded as-is, without decomposition
• No evaluation: The system cannot judge whether retrieved chunks actually answer the question
• No multi-step retrieval: Cannot chain searches where the first result informs the next query

Agentic RAG Pipeline

User Query → Agent Plans → Search Tool → Evaluate Results →
  → Need more info? → Refine query → Search again →
  → Enough context? → Synthesize → Answer

The agent acts as an intelligent orchestrator that can:

1. Decompose complex queries into sub-questions
2. Choose tools: vector search, web search, SQL query, API call
3. Evaluate whether retrieved context is sufficient
4. Iterate with refined queries until confident
5. Synthesize information from multiple retrieval rounds

How Agentic RAG Works

Core Architecture

Agent Decision Flow

An agentic RAG system typically follows this decision pattern:

1. Analyze the question: Is it simple (single retrieval) or complex (needs decomposition)?
2. Plan retrieval strategy: What sources to query? In what order?
3. Execute retrieval: Search the first source
4. Evaluate results: Do these chunks contain the answer?
5. Decide next action: Answer, refine query, or search a different source
6. Synthesize: Combine information from all retrieval steps

# Conceptual agentic RAG loop (pseudocode)
def agentic_rag(query, tools, max_iterations=5):
    context = []
    plan = agent.plan(query)  # Decompose into sub-questions
 
    for step in plan:
        # Agent decides which tool to use
        tool = agent.select_tool(step, tools)
        results = tool.execute(step.query)
        context.extend(results)
 
        # Agent evaluates if it has enough information
        if agent.has_sufficient_context(query, context):
            break
 
        # Agent refines the query based on what it learned
        step.query = agent.refine_query(step, results)
 
    return agent.synthesize(query, context)

Key Patterns in Agentic RAG

1. Query Decomposition

Break complex questions into simpler sub-queries:

Original: "Compare the revenue growth of Apple and Microsoft in Q3 2025
           and explain which company had better operating margins"

Decomposed:
  1. "Apple Q3 2025 revenue growth"
  2. "Microsoft Q3 2025 revenue growth"
  3. "Apple Q3 2025 operating margin"
  4. "Microsoft Q3 2025 operating margin"

Each sub-query retrieves focused, relevant documents instead of hoping a single broad search returns all needed information.

2. Adaptive Retrieval

The agent decides how to retrieve based on the query type:

3. Self-Evaluation

After each retrieval step, the agent evaluates:

• Relevance: Are the retrieved chunks about the right topic?
• Completeness: Do they contain enough information to answer?
• Consistency: Do multiple sources agree?
• Recency: Is the information current enough?

4. Tool Selection

Agentic RAG is not limited to vector search:

Implementation Approaches

LangChain Agent

from langchain.agents import create_react_agent
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
 
# Create retrieval tool
vectorstore = FAISS.from_texts(documents, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
 
def search_docs(query: str) -> str:
    results = retriever.invoke(query)
    return "\n".join([doc.page_content for doc in results])
 
tools = [
    Tool(
        name="SearchDocuments",
        func=search_docs,
        description="Search the knowledge base for relevant information"
    ),
]
 
llm = ChatOpenAI(model="gpt-4o", temperature=0)
agent = create_react_agent(llm, tools, prompt_template)

LlamaIndex Agent

from llama_index.core.agent import ReActAgent
from llama_index.core.tools import QueryEngineTool
from llama_index.core import VectorStoreIndex
 
# Create query engine tools
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)
 
tools = [
    QueryEngineTool.from_defaults(
        query_engine=query_engine,
        name="knowledge_base",
        description="Search the company knowledge base"
    ),
]
 
agent = ReActAgent.from_tools(tools, llm=llm, verbose=True)
response = agent.chat("What were our Q3 results?")

When to Use Agentic RAG

Trade-offs

Building Data Pipelines for Agentic RAG

Setting up the data pipeline for agentic RAG -- chunking documents, creating embeddings, building vector indices -- is iterative experimental work. RunCell (opens in a new tab) provides an AI-powered Jupyter environment where you can prototype RAG pipelines with AI assistance, debug retrieval quality interactively, and iterate on chunking strategies.

For visualizing retrieval evaluation metrics (relevance scores, latency distributions, answer quality), PyGWalker (opens in a new tab) lets you explore your RAG evaluation datasets interactively in Jupyter.

FAQ

What is agentic RAG?

Agentic RAG combines Retrieval-Augmented Generation with autonomous AI agents. Instead of a static retrieve-then-generate pipeline, an AI agent plans retrieval strategies, executes multiple searches, evaluates results, and iterates until it has sufficient context to answer accurately.

How is agentic RAG different from basic RAG?

Basic RAG performs a single retrieval and generates one answer. Agentic RAG uses an AI agent that can decompose queries, select different tools (vector search, web search, SQL), evaluate whether retrieved context is sufficient, and iterate with refined queries. It handles complex, multi-step questions that basic RAG cannot.

Is agentic RAG more expensive than basic RAG?

Yes, agentic RAG typically costs more per query because it makes multiple LLM calls (planning, evaluation, synthesis) and multiple retrieval operations. The trade-off is significantly higher answer quality for complex queries. For simple factual lookups, basic RAG is more cost-effective.

What frameworks support agentic RAG?

LangChain, LlamaIndex, and Haystack all support agentic RAG patterns. LangChain provides ReAct agents with tool use, LlamaIndex offers query planning agents, and Haystack has pipeline-based agent architectures. You can also build custom agents using the OpenAI or Anthropic function-calling APIs directly.

When should I use agentic RAG vs basic RAG?

Use basic RAG for simple factual Q&A from a single knowledge base. Use agentic RAG when queries require reasoning across multiple documents, involve comparisons, need real-time data, or when basic RAG consistently returns incomplete answers.

Conclusion

Agentic RAG addresses the core limitations of basic RAG by replacing static pipelines with intelligent agents that plan, retrieve iteratively, and evaluate their own results. It handles complex queries that span multiple documents, require reasoning, or need data from multiple sources. The trade-off is higher cost and latency per query. For most applications, the best approach is to start with basic RAG for simple queries and route complex questions to agentic RAG -- using the agent only when the simpler system would struggle.