Before we delve into agentic RAG and AI agents, let’s take a moment to acknowledge that the world of artificial intelligence is evolving at a tremendous pace. From the initial excitement surrounding large language models (LLMs) to the practical application of generative AI (Gen AI), businesses are constantly finding new ways to automate tasks and innovate faster.

Among these advancements are the concepts of autonomous AI agents and agentic AI, which represent a major leap towards more autonomous and intelligent systems that can not only process information but also proactively perform tasks and make decisions with minimal human involvement. For these agents to operate effectively in complex business environments, they need to be equipped with reliable and current knowledge. A crucial technique that addresses this need is retrieval-augmented generation (RAG).

This article will serve as a comprehensive guide to agentic RAG, with emphasis on the relationship between RAG and the agentic nature of AI agents. Let’s start with a brief recap of RAG and AI agents.

What are AI agents?

AI agents are autonomous software entities that can perceive their environment through sensors and act upon that environment through effectors to achieve specific goals.

They are characterized by their ability to make decisions independently, learn from their experiences, and often interact with other agents or humans. Key characteristics of AI agents include:

• Autonomy
• Proactiveness
• Reactivity
• Goal-directedness

What is RAG?

Retrieval-augmented generation (RAG) is a framework designed to enhance the capabilities of LLMs by allowing them to access and incorporate information from external knowledge sources during the generation process.

A RAG pipeline has two key components:

• A retrieval component that comprises of a vector database and an embedding model that is responsible for searching and fetching relevant information from external data sources
• A generation component that uses an LLM to generate coherent responses

So, instead of relying solely on the data they were trained on, LLMs using RAG first retrieve relevant documents or information snippets based on the user’s query and then use this retrieved information to generate more accurate, contextually relevant, and up-to-date responses. This approach helps to mitigate issues like hallucination and knowledge cut-off often associated with standalone LLMs.

Now let’s talk about what makes RAG agentic.

What is agentic RAG?

Agentic RAG is an advanced form of retrieval-augmented generation where AI agents are strategically integrated into the RAG pipeline to enhance its capabilities. Instead of a fixed “retrieve then generate” step, the model treats the retriever as a tool it can call at any point—deciding when to fetch data, which queries to issue, and how to link evidence across multiple hops.

Agentic RAG also tracks context in the form of short-term working memory and long-term state. It remembers which sources it has consulted and what each one revealed. That record prevents repeat searches, supports reasoning over several rounds, and lets the system recall past findings or user preferences.

In addition to providing a dynamic interface for data retrieval, Agentic RAG incorporates multiple specialized agents that collaborate within the pipeline. Each agent filters noise, assesses the relevance of retrieved data, and dynamically adjusts retrieval parameters based on real-time feedback.

In short, Agentic RAG represents a significant step toward building more intelligent, autonomous, knowledge-driven AI systems.

How does agentic RAG work?

We know that traditional RAG typically involves a single query, retrieval of relevant documents, and generation of an answer based on the retrieved context and the original query. Agentic RAG, in contrast, breaks down the process into a series of steps executed by different intelligent agents.

Here’s a breakdown of the steps typically followed in the agentic RAG pipeline:

Advanced query processing: Instead of directly embedding the user’s query, the query understanding and decomposition agent first analyzes the query for its intent, key entities, and complexity. It might break down a complex query into several sub-queries.

Intelligent and multi-source retrieval: The retrieval agent takes the processed query (or sub-queries) and retrieves information from various knowledge sources. This can include vector databases (using embeddings and similarity search), but also knowledge graphs (using graph traversal), the web (using targeted search strategies), and potentially other structured or unstructured data sources. The choice of retrieval method and source can be dynamic based on the query analysis.

Contextual information fusion and ranking: The retrieved information from different sources is gathered by the information fusion agent, which appends context and assesses the relevance and quality of each piece of information. It might filter out redundant or contradictory information and rank the remaining content based on its relevance and reliability.

Reasoning and plan formulation: The reasoning and planning agent takes the gathered information and develops a step-by-step plan to construct the answer and guide the generation process. It can perform reasoning tasks like synthesizing information from multiple documents, identifying relationships, and drawing inferences.

Enhanced generation with reasoning: The generation agent then uses an LLM, but with a more structured and reasoned context provided by the previous steps, to generate a more comprehensive and accurate response.

Response evaluation and refinement: The refinement agent assesses the quality of the generated response. If it doesn’t meet certain criteria, this agent can trigger further iterations of the retrieval, reasoning, and generation steps, potentially adjusting the strategies of other agents to improve the response.

Coordinated workflow management: Throughout this entire process, the orchestration agent manages the flow of information and coordinates the activities of all the individual agents, ensuring each step is executed in the correct sequence and that information is passed efficiently between them.

Final response delivery: Finally, the output agent presents the generated response to the user.

Why do businesses need agentic RAG?

To put it differently, why do businesses need AI agents when they already have RAG and LLMs?

Initially, RAG emerged as a way to enhance language models by grounding their outputs with real-world, retrieved information. However, as use cases become more complex, it is evident that a static retrieval mechanism cannot keep pace with the fluidity and depth of modern information ecosystems. Here’s what agentic RAG helps businesses with:

Overcoming static limitations

Traditional RAG systems are effective at supplementing LLMs with external data, yet they struggle to adapt on the fly. The integration of autonomous AI agents continuously manages, refines, and verifies retrieved information, ensuring that the generative component is fed with the most relevant, up-to-date content.

Coping with the explosion of data

With the exponential growth of digital information and increasingly nuanced queries, organizations need a more dynamic, self-regulating mechanism. Agentic RAG empowers businesses to leverage LLMs for enhanced insights and continuous learning through intelligent information retrieval and processing.

Pushing the boundaries of autonomy

As AI models advance, so does the ambition to build systems that can reason, plan, and adapt independently. Embedding autonomous agents within the RAG pipeline allows for a higher level of orchestration as these intelligent systems are actively engaged in decision-making processes regarding what data to retrieve and how to filter it, and how to integrate it with generative outputs.

Accessing context-aware responses in real-time

The development of agentic RAG reflects an industry-wide demand for real-time intelligence. In other words, businesses are looking toward systems that can self-correct and update their knowledge base continuously. This is crucial for accuracy and applications that require timely insights in dynamic environments.

Agentic RAG vs. Traditional RAG

While traditional RAG itself is a highly effective method for enhancing LLMs with external knowledge, agentic RAG differentiates itself by engaging autonomous AI within the core processes of information retrieval and content generation. This leads to a more dynamic and intelligent approach to knowledge integration, enabling capabilities that go beyond the traditional RAG pipeline.

Here are the differences between traditional RAG and agentic RAG:

Intelligence in retrieval

Traditional RAG typically employs a more straightforward retrieval mechanism, relying on keyword matching or semantic similarity against a pre-indexed knowledge base. The retrieval strategy is generally fixed for a given query.

Agentic RAG leverages the intelligence of autonomous agents to make decisions about the retrieval process. These agents can formulate more sophisticated queries, explore multiple retrieval strategies, and even perform iterative retrieval based on the initial results.

Handling multiple data sources

Traditional RAG might be configured to search across multiple data sources, but the process is often predefined and less dynamic.

Agentic RAG enables agents to intelligently select and query various data sources based on the context of the query and the information already gathered. Agents can decide which sources are most likely to contain relevant information and how to best access them.

Complexity of workflows

Traditional RAG generally follows a linear workflow: retrieve relevant documents, augment the prompt, and generate the response.

Agentic RAG supports more complex and dynamic workflows. Agents break down complex queries into smaller steps, orchestrate multiple retrieval and processing stages, and even involve different specialized agents for specific tasks.

Adaptability

Traditional RAG relies on a retrieval and generation process that is often less adaptive to the specific nuances of individual queries or evolving information landscapes.

Agentic RAG exhibits greater adaptability as agents learn from past interactions, adjust their retrieval strategies over time, and tailor the information processing based on the specific requirements of the user and the context of the conversation.

Accuracy and contextual understanding

Traditional RAG’s accuracy largely depends on the quality of the retrieved documents and the LLM’s ability to synthesize the information. Contextual understanding is primarily handled by the LLM itself.

Agentic RAG benefits from AI agents that play a more active role in ensuring accuracy by cross-referencing information from multiple sources, filtering out irrelevant or low-quality data, and reasoning over the retrieved content to provide the LLM with more refined and contextually rich information for generation.

How are businesses using agentic RAG?

Integrating autonomous agents within RAG systems is a strategic reinvention of enterprise data workflows that enables organizations to achieve unprecedented levels of contextual intelligence and precision. In practice, agentic RAG enables context-aware and multi-step responses that go far beyond simple chatbot interactions.

Here are the functional areas and use cases agentic RAG caters to:

Customer support

Companies are deploying agentic RAG in call centers and customer service portals to quickly retrieve up-to-date data (such as policy documents, troubleshooting guides, or real-time order information) and generate tailored responses. Real-time question-answering reduces resolution times and cuts down on manual workload.

Internal knowledge management and document automation

Organizations use agentic RAG to automatically tag, organize, and synthesize large volumes of internal documents, transforming them into searchable knowledge bases that support faster decision-making.

Sales, marketing & business intelligence (BI)

Sales and marketing teams use agentic RAG to generate personalized communications, automate lead prospecting, and produce comprehensive market analysis reports. This improves productivity and decision-making.

Financial services and risk management

In finance, agentic RAG systems help in tasks like credit risk analysis and compliance by retrieving the latest market data and regulatory documents, then synthesizing insights for informed decision-making. Research also demonstrates applications in building model risk management crews for portfolio analysis.

What do you need to implement the agentic RAG architecture?

Agentic RAG architecture combines advanced retrieval methods with intelligent, autonomous reasoning to deliver highly accurate outputs. But how can companies transform their existing data workflows into an agile, multi-step, context-aware system? Achieving this transformation requires three core capabilities.

First, access to cutting-edge LLMs and generative AI tools is essential. These models provide the requisite cognitive foundation that drives intelligent decision-making and nuanced reasoning. Second, a robust data integration layer is needed to seamlessly connect various internal and external data sources—from structured enterprise databases to dynamic cloud services and real-time APIs. Finally, the missing enabler is a powerful AI agent builder platform that offers intuitive development environments, enabling non-technical users to design, deploy, and manage AI agents without friction.

Such an AI agent platform serves as a centralized hub that automates the tedious process of data curation and workflow orchestration. It streamlines the integration of disparate data sources, ensures consistent data quality, and accelerates the development cycle—making sophisticated, autonomous AI solutions accessible to organizations of all sizes.

Build a reliable agentic RAG architecture with Astera

Astera offers a visual, drag-and-drop environment that simplifies the creation, testing, and deployment of AI agents built around your enterprise data. When you understand your data, you can directly translate that expertise into building AI that makes a real impact.

Astera AI Agent Builder is built for enterprises that wish to deploy AI agents across various functions. Organizations can develop AI solutions such as:

• RAG agents that pull answers and insights from enterprise documents, contracts, and knowledge bases
• Workflow automation agents that interconnect processes across departments for smooth operations
• Customer support agents that understand and respond to customer needs, escalating complex issues when necessary
• Sales agents that generate personalized outreach, respond to leads, and produce actionable account insights
• Marketing agents that craft campaign content, offer optimization suggestions, and analyze data around key metrics
• HR agents that address employee inquiries regarding policies, benefits, and onboarding procedures
• IT and helpdesk agents that resolve technical issues and automate ticket creation
• Finance and procurement agents that generate budget summaries, process vendor information, and handle finance-related queries

Here’s why your teams will love it:

• Empowerment Across Teams
• Rapid AI Agent Development
• Direct Data Connectivity
• Seamless Experimentation

Ready to transform your business operations with agentic RAG? See how Astera AI Agent Builder empowers your teams to build and deploy intelligent AI agents that drive business outcomes.