In the previous blog post on evaluating Retrieval-Augmented Generation models, we discussed what RAG is, why you should test RAG models, what to test, which metrics to measure, methods to evaluate RAG models, and more.
While the RAG frameworks integrate the large language models (LLMs) with external data retrieval, enabling you to deliver accurate and contextually aware responses, it falls short when handling nuanced queries and in dynamically changing contexts.
Enter Agentic Retrieval Augmented Generation (Agentic RAG).
It refers to using autonomous AI agents to facilitate the retrieval process. Unlike traditional systems, these agents are not passive retrievers but rather active participants who adapt and respond proactively, improving decision-making and problem-solving.
With that, let’s dive into what Agentic RAG is and how it works.
What is Agentic RAG?
It is an AI agent-based implementation of Retrieval-Augmented Generation, where autonomous agents dynamically retrieve relevant documents and integrate information into AI-generated responses. These intelligent AI agents not only retrieve information but also execute tasks based on complex workflows.
The shift from information retrieval to an interactive engagement with data not only significantly improves decision-making but also makes Agentic RAG a more advanced approach to information retrieval than traditional RAG systems.
Before getting into how it works, let’s get some basics out of the way.
What is Retrieval-Augmented Generation (RAG)?
The core purpose of RAG is to boost the accuracy and answer relevance of AI outputs by incorporating external knowledge sources. The standard RAG pipeline includes two key components, a retrieval component, and a generative component, akin to stocking a library and indexing its contents, ensuring that the system can efficiently access the required data when neededand produce a coherent final response.
To know more about how traditional RAG works and how to evaluate the performance of RAG models, we recommend you check out our primer on the topic 👇
Now, let’s understand agents.
What is an AI Agent?
An AI agent is an autonomous entity that adapts to its environment, makes decisions, and takes action to achieve specific goals.
In the context of Agentic RAG systems, AI agents extend the capabilities of large language models by accessing external knowledge sources to perform tasks. These agents are autonomous in their decision-making and proactive problem-solving abilities.
Memory, too, plays a key role in the functioning of AI agents, allowing them to recall past tasks, plan detailed actions, and adapt their behavior. Semantic caching enables these agents to store previous retrieved context and query results, making task execution more efficient and improving the accuracy of the final response.
Fundamentals of RAG Agents
RAG agents are the core component of Agentic RAG systems, responsible for retrieving relevant information from external data sources and integrating it into the AI system’s knowledge base. These agents are designed to work in conjunction with large language models, enabling them to handle complex tasks and provide accurate answers. RAG agents can be trained on various data sources, including structured and unstructured data, and can be integrated with multiple tools and external knowledge sources.
How Agentic RAG Retrieves Context and Generates Accurate Responses
It works by incorporating one or more AI agents into RAG systems, where each agent specializes in a certain domain or data source.
Let’s look at what the process looks like:
Step 1: You input your query, and an agent rewrites it, correcting errors if any (you would have experienced this in ChatGPT, for instance)
Step 2: Another agent then decides whether it needs more details to answer your question, and then the query is sent to the language model as a prompt
Step 3: A third agent reviews retrieved documents and determines which source is the most contextual and relevant, and retrieves it accordingly.
Step 4: Now, a final agent checks if the generated response is relevant to the query and context. If yes, it returns the response. And if not, the process continues. This process makes the RAG more robust since, at every step, the agents ensure that individual outcomes are aligned with the final goal.
Here’s a graphical representation of how it works:
The Four Pillars of Agentic RAG
Agentic RAG is built on four pillars:
- Autonomy
AI agents act independently. They make decisions. They execute tasks without waiting for instructions. This isn’t optional, it’s what allows Agentic RAG to adapt to any context, any query, in real time. - Dynamic Retrieval
Static search is dead. Agentic RAG agents scan multiple sources and decide instantly what’s relevant. APIs, databases, external knowledge, they pull it all. The result: retrieved context that is always current, always precise. - Augmented Generation
Information alone isn’t enough. Agents integrate relevant documents into the generated response, giving the final response clarity, context, and authority. No filler. No ambiguity. Just accurate, actionable answers. - Feedback Loop
Every output is tested. Every gap is corrected. The system learns. The system improves. Over time, RAG performance gets sharper, responses get smarter, and answer relevance climbs.
These systems execute complex tasks across large and diverse datasets, dynamically adapting their workflows in real time to optimize performance using structured data, data sources, and external data sources.
Integrating retrieved data into coherent responses makes Agentic RAG systems more efficient and responsive than traditional RAG systems.
Real-time information retrieval
Real-time information retrieval is the backbone of Agentic RAG systems. These systems access up-to-date information from various sources to maintain accuracy and relevance in their outputs. Agentic RAG uses APIs and databases to dynamically fetch updated retrieved information, so the responses are both accurate and relevant data.
Think about customer support. These systems gather info from multiple places, connect the dots, and help solve problems in real-time. The same goes for tracking competitors or spotting market trends, you get timely insights without digging through dashboards.
Because Agentic RAG can tap into different external sources, it’s able to handle a wide range of questions and use cases on the fly.
Adaptive task execution
Complex queries don’t scare Agentic RAG. Agents break questions into subtasks. They run them in parallel. They validate retrieved documents, combine insights, and deliver a generated response that is coherent and complete. Query planning agents orchestrate it all, making sure every piece of retrieved context contributes to the correct answer.
The query planning agents are key in this process. (We will discuss query planning agents in the components section). They manage task workflows by breaking down complex queries and combining responses to provide coherent results to your query, allowing you to explore complex topics and derive insights.
Iterative context validation
As you can imagine, context validation is about repeatedly analyzing the query and feedback to refine the responses until its understanding reaches the desired context.
Dynamically adjusting workflows in real time makes these systems more efficient and responsive. By incorporating agents capable of tool use, agentic RAG enhances the quality of the retrieved context, improving the accuracy of responses through better access to specialized knowledge and validation of the retrieved information before further processing. The agent’s reasoning capabilities further improve the validation processes within Agentic RAG systems.
Key components
These systems comprise several types of agent systems, where each agent plays a specific role in the retrieval-augmented generation pipeline. These RAG agents provide the necessary resources and functionalities to achieve their tasks within retrieval systems.
The Agentic RAG architecture can either be simple (single-agent router) or very complex (multi-agent system).
1. Router agents
Router agents enable agents to decide which external knowledge sources and tools to use based on user queries. Acting as traffic controllers, these agents assess the task and direct it to the right resource so the most relevant documents are retrieved. A retrieval agent enhances this process by improving the accuracy of responses, allowing for autonomous task performance, and facilitating better collaboration with humans through access to specialized knowledge sources and validation of retrieved information before processing.
2. Query planning agents
Query planning agents manage and orchestrate responses from multiple agents to achieve coherent results. These agents break down complex questions into smaller subqueries so that each part of the query is handled efficiently. The user query is essential for retrieving relevant information from indexed documents, allowing AI agents to perform reasoning and generate insightful responses based on user input. They decide on actions and execute them, ensuring effective query management and accurate and relevant responses.
3. ReAct framework
This framework integrates reasoning and action so agents can handle complex multi-part queries effectively using multi-step reasoning. ReAct agents adjust subsequent stages based on the results of each step so the execution of multi-step workflows is improved. Tool use is a pivotal enhancement in AI systems, specifically in the context of agentic RAG, as it allows for greater flexibility and autonomy in task performance.
The feedback loop within the ReAct framework ensures long-term performance improvement by allowing agents to refine responses and adapt tasks over time.
Agentic RAG has many applications across industries and functions. By reducing manual workloads and improving team efficiency, these systems can transform customer support, healthcare decision-making, educational tools, business intelligence, and scientific research.
4. Support automation
It can be applied in real-time question-answering systems. This allows companies to answer customer questions quickly or take contextual action without delays.
For instance, one of our customers, Espresso Capital, uses an AI-powered question-answer interface to automate document analysis, saving them 1,250 lawyer hours annually and significantly reducing manual workload. It helped them achieve $625,000 in annual cost savings, optimizing operational efficiency.
Some more industry examples include:
Healthcare decision-making
In healthcare, Agentic RAG synthesizes retrieved documents and medical data so that the clinical staff can make quick but informed decisions. These systems stay up-to-date, providing accurate retrieved context that helps identify new pathways and improves answer relevance for patient care.
Educational tools
For education, Agentic RAG helps provide personalized and adaptive content to individual learning preferences. These systems create an interactive learning environment by adapting content to students’ learning styles.
It also enables group projects by allowing collaborative access to shared resources and materials. The flexibility of these systems allows them to be applied to various sectors such as healthcare, finance, and education.
Business intelligence
It improves business report generation. It automates the retrieval and analysis of key performance indicators (KPIs), saving analysts time. Hence, they are free to focus on tasks that truly matter.
They also play a key role in scientific research by finding relevant studies for research projects, extracting key findings from multiple studies, and providing researchers with a cohesive view of the topic.
Needless to say, Agentic RAG synthesizes information from multiple sources and diverse sources, so overall research quality and comprehensiveness are improved.
Implementing Agentic RAG
Implementing the framework requires a strategic approach and careful consideration of several factors.
Agent frameworks simplify building Agentic RAG systems by providing integration of tools and resources. On that note, if you want to deep dive into agent frameworks, here’s a primer on the top 5 multi-agent frameworks 👇
Agentic RAG can be implemented using either a language model with function calling or an agent framework.
From an infrastructure standpoint, creating robust api keys helps connect Agentic RAG systems to existing enterprise infrastructure. Implementing OAuth 2.0 is critical for secure integration.
Further, integrating Agentic RAG with existing systems improves automation, accuracy, responsiveness, and, thereby, overall operational efficiency.
RAG Evaluation
A Retrieval-Augmented Generation system is only as strong as its evaluation process. RAG evaluation focuses on how effectively the model retrieves relevant documents, integrates them into the generated response, and delivers an answer aligned with the ground truth. Done right, this process reveals whether your system is returning correct answers, leveraging retrieved context accurately, and producing responses that users can trust.
- Retrieval level: Evaluation metrics assess the quality, accuracy, and relevance of the retrieved documents. They measure how well the system identifies and ranks the most relevant context to support the final response.
- Generation side: Metrics evaluate answer relevance, the faithfulness of the generated response to the retrieved context, and how closely it aligns with the reference answer or correct answer. These criteria reveal whether the language model is producing grounded, contextually accurate outputs.
A rigorous evaluation process is critical for improving RAG performance. It exposes gaps in retrieval accuracy, identifies where generated responses deviate from the ground truth, and ensures the system remains context-aware as test data and real-world scenarios evolve. In short, RAG evaluation isn’t just about scoring outputs, it’s about validating that every step, from retrieved documents to final response, works together to deliver reliable, relevant answers.
Evaluation Metrics in RAG Systems
Metrics are central to understanding a RAG system’s performance. On the retrieval side, indicators such as Precision@k, Recall@k, MRR, and nDCG measure how effectively the system surfaces the most relevant documents. These metrics reveal whether the retrieved context supports accurate and useful responses.
On the generation side, metrics assess the quality of the final response. Traditional measures like BLEU or ROUGE evaluate overlap with a reference answer, while newer semantic-based metrics measure answer relevance, faithfulness, and alignment with the ground truth. Together, these metrics provide a complete picture of system performance, highlighting gaps in retrieval accuracy, generation quality, and the overall RAG performance.
Summing up...
Traditional RAG is reactive, where agentic systems analyze context and user intent to retrieve information from multiple sources, making agentRAGrag systems more reliable in handling complex workflows.
Agentic RAG systems are proactive in adapting to context and engaging multiple AI agents, breaking free from the limitations of static queries in traditional RAG systems. They can optimize results through iterative processes so the relevance of responses improves over time, thus addressing the limitations of traditional language models by incorporating relevant, up-to-date content for more accurate responses.
In summary, they are a big leap forward from the traditional approach. These systems improve accuracy, relevance, and adaptability, so they are useful across industries.
As we look to the future, the continued development and adoption of Agentic RAG will change how AI systems interact with data and deliver personalized solutions.
Zams takes that same leap for sales - an AI command center that turns busywork into leverage and keeps teams focused on revenue.
FAQ
1. What is Agentic RAG and how is it different from traditional RAG systems?
Agentic Retrieval-Augmented Generation (Agentic RAG) is an advanced approach to retrieval augmented generation that uses autonomous AI agents to actively manage the retrieval process. Unlike traditional RAG systems, which passively fetch data, Agentic RAG agents adapt dynamically, plan tasks, validate retrieved context, and ensure that the final response is accurate and relevant. This makes them far better suited for complex workflows, nuanced queries, and real-time information retrieval.
2. How do Agentic RAG systems improve answer relevance and RAG performance?
Agentic RAG enhances answer relevance and overall RAG performance through iterative retrieved context validation and adaptive task execution. Multiple specialized agents collaborate, rewriting queries, selecting relevant documents, validating retrieved documents, and refining the generated response, to align closely with the reference answer or ground truth. This continuous feedback loop ensures that the system delivers highly contextual, accurate, and updated information.
3. What are the key components involved in evaluating Agentic RAG systems?
When evaluating RAG systems, key components include router agents, query planning agents, and reasoning frameworks like ReAct. Router agents decide which external sources to query, while query planning agents break down complex queries and orchestrate subtasks. Together, they ensure that the retrieved documents are relevant and that the generated response meets your evaluation criteria, such as accuracy, contextual alignment, and consistency with the correct answer.
4. What industries benefit most from Agentic RAG and why is it significant?
Agentic RAG’s dynamic retrieval and iterative evaluation process make it ideal for sectors that rely on real-time, accurate information, such as healthcare, education, business intelligence, and customer support. By integrating up-to-date retrieved documents into the generated response, these systems improve decision-making, streamline workflows, and ensure the final response is aligned with the reference answer, ultimately delivering more reliable and adaptive AI solutions.
5. What does the evaluation process of Agentic RAG look like?
The evaluation process for Agentic RAG involves testing how well autonomous agents handle query rewriting, task planning, retrieval, and validation. It starts with test data, where the system retrieves and evaluates retrieved documents against the reference answer or correct answer. Then, evaluation metrics are applied to measure how accurately the generated response reflects the ground truth. This step-by-step evaluation ensures the system is optimized for both retrieval quality and overall RAG performance.
