# Agentic RAG

> An advanced RAG architecture where an AI agent autonomously decides which questions to ask, which tools to use, when to retrieve information, and how to aggregate results. Represents a major trend in 2026 for more intelligent and adaptive retrieval systems.

- **URL:** https://www.techment.com/blogs/rag-in-2026/
- **Category:** Concepts & Definitions
- **Tags:** Rag, Ai Agents, 2026 Trends
- **Featured:** yes
- **Updated:** 2026-04-22 13:39
- **Canonical page:** https://vectordb.ever.works/th/items/agentic-rag

## Details

## Overview

Agentic RAG is an evolution of traditional Retrieval-Augmented Generation that incorporates autonomous decision-making capabilities. Instead of following a fixed retrieval pipeline, an AI agent dynamically determines the retrieval strategy based on the query.

## Key Characteristics

- **Autonomous Decision Making**: Agent decides which questions to ask and which tools to use
- **Dynamic Tool Selection**: Chooses between vector search, graph traversal, SQL queries, web search, etc.
- **Adaptive Retrieval**: Adjusts retrieval strategy based on intermediate results
- **Result Aggregation**: Intelligently combines information from multiple sources
- **Self-Correction**: Can refine queries and re-retrieve if initial results are insufficient

## How It Works

1. **Query Analysis**: Agent analyzes the user question to understand requirements
2. **Planning**: Determines which retrieval methods and tools are needed
3. **Execution**: Executes retrieval steps, potentially in parallel
4. **Evaluation**: Assesses quality of retrieved information
5. **Iteration**: Refines and re-retrieves if needed
6. **Synthesis**: Generates final answer by aggregating results

## Advantages Over Traditional RAG

- More accurate and relevant retrieval for complex queries
- Handles multi-step reasoning tasks
- Adapts to different types of questions
- Better handling of ambiguous queries
- Can combine multiple data sources intelligently

## Example Use Cases

- Multi-hop question answering
- Complex research queries requiring multiple sources
- Dynamic data exploration
- Enterprise knowledge bases with heterogeneous data
- Scientific literature review

## Implementation Approaches

- ReAct (Reasoning + Acting) pattern
- LangChain Agents with tool selection
- Custom agent frameworks with retrieval tools
- LlamaIndex agent modules

## Trends in 2026

Agentic RAG has emerged as a major trend, with enterprises increasingly adopting it for more reliable and accurate AI systems that align with priorities around accuracy, explainability, and compliance.

## Pricing

Implementation-dependent based on chosen frameworks and LLM providers.

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