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    Decorative pattern
    Decorative pattern
    1. Home
    2. Concepts & Definitions
    3. Agentic RAG

    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.

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    Websitewww.techment.com
    PublishedMar 15, 2026

    Categories

    1 Item
    Concepts & Definitions

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    #Rag#Ai Agents#Llm

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    RETA-LLM

    RETA-LLM is a toolkit designed for retrieval-augmented large language models. It is directly relevant to vector databases as it involves retrieval-based methods that typically leverage vector search and vector databases to enhance language model capabilities through external knowledge retrieval.

    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.