Agentic Retrieval-Augmented Generation (Agentic RAG) is an advanced paradigm that integrates autonomous AI agents into the retrieval-augmented generation pipeline. Unlike basic RAG, which follows a static retrieve-then-generate pattern, Agentic RAG employs agents capable of autonomous decision-making, iterative refinement, and dynamic workflow orchestration to handle complex, multi-step information needs.

Traditional RAG systems retrieve documents from a knowledge base and concatenate them into the LLM context for generation. This single-pass approach fails on tasks requiring multi-hop reasoning, adaptive query strategies, or cross-source synthesis. Agentic RAG addresses these limitations by embedding agent capabilities directly into the retrieval loop.

The key insight is that retrieval should be treated as an agentic action rather than a static preprocessing step. The agent decides when to retrieve, what to retrieve, and how to reformulate queries based on intermediate reasoning.

Singh et al. (2025) propose a comprehensive taxonomy of Agentic RAG architectures organized around four core agentic design patterns:

• Reflection – Agents evaluate their own retrieval results and generation quality, triggering re-retrieval when confidence is low
• Planning – Autonomous decomposition of complex queries into retrieval sub-plans with dependency tracking
• Tool Use – Integration of heterogeneous retrieval tools (dense retrieval, sparse search, SQL, APIs) selected dynamically per sub-query
• Multi-Agent Collaboration – Distributed retrieval tasks across specialized agents with coordination protocols

The taxonomy classifies systems into: single-agent RAG, multi-agent RAG, hierarchical RAG, corrective RAG, adaptive RAG, and graph-based RAG.

In Agentic RAG, the agent autonomously plans retrieval strategies based on query complexity. Given a user query $q$, the agent generates a retrieval plan $P = \{(q_1, s_1), (q_2, s_2), \ldots, (q_n, s_n)\}$ where each $q_i$ is a sub-query and $s_i$ is the selected retrieval source.

The planning process uses a reward signal $R(P, q)$ that estimates plan quality:

$$R(P, q) = \sum_{i=1}^{n} \alpha_i \cdot \text{rel}(q_i, q) \cdot \text{cov}(D_i, q)$$

where $\text{rel}(q_i, q)$ measures sub-query relevance to the original query, $\text{cov}(D_i, q)$ measures coverage of retrieved documents $D_i$, and $\alpha_i$ are learned weighting coefficients.

Unlike static query expansion, Agentic RAG reformulates queries iteratively using feedback from previous retrieval rounds. The agent maintains a belief state $b_t$ at each step $t$:

$$b_{t+1} = \text{Update}(b_t, D_t, \text{Eval}(D_t, q))$$

If the evaluation function $\text{Eval}(D_t, q)$ indicates insufficient coverage or relevance, the agent generates a reformulated query $q_{t+1}$ conditioned on both the original query and the gap analysis.

from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain_core.prompts import PromptTemplate
 
def build_agentic_rag(llm, retriever, tools):
    """Build an Agentic RAG pipeline with autonomous retrieval planning."""
    retrieval_tool = Tool(
        name="knowledge_retrieval",
        func=retriever.invoke,
        description="Retrieve relevant documents from the knowledge base"
    )
    all_tools = [retrieval_tool] + tools
 
    prompt = PromptTemplate.from_template(
        "You are a retrieval agent. Analyze the query complexity, "
        "plan retrieval steps, and iteratively refine until sufficient "
        "evidence is gathered.\n\nQuery: {input}\n{agent_scratchpad}"
    )
 
    agent = create_react_agent(llm, all_tools, prompt)
    return AgentExecutor(agent=agent, tools=all_tools, max_iterations=10)

Agentic RAG has demonstrated strong results across domains:

• Healthcare – Multi-hop diagnostic reasoning linking symptoms to disease pathways
• Finance – Adaptive risk modeling with iterative evidence gathering from market data
• Legal – Cross-reference analysis across statutes, case law, and regulatory documents
• Education – Personalized tutoring with dynamic knowledge retrieval

• Singh et al. (2025) -- Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
• AgenticRAG-Survey GitHub Repository
• Agentic RAG Frameworks -- Extended Analysis

• Deep Search Agents – Autonomous multi-step retrieval with dynamic planning
• Task Decomposition Strategies – Systematic decomposition for complex reasoning tasks
• MCTS for LLM Reasoning – Tree search methods for LLM reasoning