The scenario is like this– In early 2023, GPT, or Generative AI, swept everything by surprise with its content-generation and QnA abilities. For most, it was a transformative shift. However, the limitations of static training data soon emerged. Generative AI exhibited inaccuracy and hallucinations for real-time response generation and autonomous task management with the retrieved information. Then RAG, or retrieval-augmented generation, appeared to address the limitations of Generative AI by enhancing LLMs with real-time data retrieval to provide contextual answers. 

But, as people continue to seek answers to nuanced and context-aware questions, RAG lacks adaptability and multi-step reasoning for complex task management. This is where Agentic Retrieval-Augmented Generation, or RAG, comes into play. By elevating traditional RAG’s approach of searching information and leveraging contextual meaning from vast data points, Agentic RAG ensures you can redefine AI-driven information retrieval and contribute to real-world applications for multi-step complex workflows, including simple to common workflows. 

Our article highlights the fundamentals of agentic RAG, including its use cases, benefits, and implementation strategies.

The fundamental understanding of agentic RAG is embedding autonomous AI agents in the RAG pipeline. By embedding RAGs with AI agents, traditional RAGs can become independent agents that can gain extended retrieval capabilities for multi-step reasoning, planning, tool integration, and adapting workflows to meet complex task requirements. 

What is Retrieval-Augmented Generation (RAG)​?

LLM systems that fetch information from external systems and public knowledge are RAG or retrieval-augmented generation. Since RAG works only on static workflows, including external knowledge, it tends to create insufficient answers to solve problems. These systems do not apply reason or validation to the retrieved information, so RAG can struggle to make decisions and complete tasks.

Limitations of traditional RAGs

RAG is suitable for static workflows. This means that RAG can work when triggered by a predefined set of workflows that require minimal contextual complexity. There are some substantial reasons for traditional RAGs to fall short of meeting advanced and dynamic query resolution. 

These limitations include

• Lack of contextual understanding — When traditional RAG systems retrieve data, they apply keyword-based search functions rather than semantic understanding, which makes them struggle to identify relevance to the query. 

• Inability to identify expert knowledge —Traditional RAG systems lack contextual integration, making it challenging to understand high-quality data. As a result, RAGs surface only generic responses. 

Difficulties with information prioritization — Traditional RAG systems are not equipped to sieve information through large datasets and answer nuanced queries. Failure to work with high-quality data, RAGs can surface generic Information.

Agent-based RAG implementation is known as agentic RAG in a very fundamental stage. It means embedding the power of the agency or agents with the LLM-based RAGs to help execute multi-task or complex tasks requiring multi-step reasoning, strategies, and utilization of multiple tools is agentic RAG. 

Unlike traditional RAGs, agentic RAG systems can interact with multiple systems to fetch the correct documents, analyze data, and make multi-step reasoning to generate a response. In essence, agentic RAG systems combine multiple agents to delegate specific roles, escalate the implementation of documents, and manage new requirements with sub-agents. 

Picture agentic RAG systems as a whole team of experts dedicated to executing a task. For example, if you need to deliver a furniture set to a client, you need various people at your disposal to accomplish the assignment. You need a team of dispatchers, a logistics vehicle, and a driver.

Imagine all of this workforce as agents. So, during the transit, if there is a message popping up and saying that the delivery location would be something else rather than the one registered with the company, your people supervising the task would plan accordingly, make strategic decisions, and execute the task. 

Likewise, agentic RAGs can adapt to changing workflows, accomplish tasks by referring to new documents, and make strategic decisions.  

An agentic RAG system aims to address the limitations of LLM-based RAGs by combining more intelligent agents and external tools to tackle complex tasks. 

How does agentic RAG work?​ 

The key goal of a RAG system is to retrieve information from various sources. Hence, when agentic RAGs are used for retrieval components, they become retrieval agents. There are multiple retriever systems to which agents have access and become retrieval agents, such as, 

• Vector database for vector search 

• Web search 

• Any API to access information from external sources, such as emails and chat data from channels, including MS Teams, Slack

• And many more. 

Once access is provided to the retriever, agentic RAG systems reason and act over the retrieval scenarios in the following manner. 

• Decide if it should retrieve information or not

• Decide which tool is better for retrieving relevant information 

• Formulate the query itself 

• Evaluate the context of the retrieved information and determine whether it needs to be re-retrieve 

Key features of agentic RAG

Agent RAG has some crucial and significant features to help execute tasks. These features include, 

• Orchestrated question and answering

Agentic RAG applies a unique orchestration process to streamline question-answering workflows. It breaks down tasks into smaller, more manageable tasks to assign to each agent, ensuring seamless coordination of optimal outcomes.

• Goal-driven 

Agentic RAG agents are designed to recognize and pursue specific objectives to manage more complex and meaningful tasks. 

• Advanced planning and reasoning 

Agents in the RAG pipeline use advanced reasoning and planning to determine the most effective strategies for retrieving information, analyzing it, synthesizing it, and answering complex questions. 

• External tool integration 

Agentic RAG integrates with many data sources and external tools, such as search engines, databases, and APIs, to enhance understanding and make informed decisions. 

• Context-awareness 

Agentic RAG systems develop enhanced context over time. They refer to past interactions, user preferences, and evolving scenarios to make informed decisions and take appropriate actions. 

• Continuous learning 

Intelligent RAGs learn and evolve. Their external knowledge expands with changing scenarios, helping them improve their capacity and address increasingly complex questions. 

• Customizability and flexibility 

Agentic RAG offers extensible customizability and flexibility for specific tasks per domain or industry needs. The best thing is that agents can be designed or trained to handle particular tasks and unique requirements. 

• Enhanced accuracy and efficiency

Agent-based systems integrate with large language models, allowing agentic RAG to generate accurate and relevant information while reducing hallucinations compared to traditional RAGs. 

Traditional RAG, popular as Vanilla RAG, is designed to retrieve information and generate responses but lacks expandability to manage complex and nuanced queries. However, using tools or external tool integration makes RAG more intelligent and powerful in solving complex and dynamic queries with multi-step reasoning. Here is our table of differences between Agentic RAG and traditional RAG based on their key features. 

This comparison table provides enough insights into how Agentic RAG can be powerful when its optimization abilities are combined with context-based retrieval strategies. At the same time, Vanilla RAG or traditional RAG lacks significant retrieval abilities, as it depends on manual optimization and static processes.

Embedding agents in the RAG pipeline builds the foundation for agentic RAG. Based on reasoning and analytical abilities at different stages of task execution, there are several AI agents in the RAG pipeline that retrieve and generate information. These agents constitute how a variety of agentic RAG models execute tasks. 

Routing agents 

The routing agent leverages LLMs to determine the most relevant RAG pipeline for the query. Using agentic reasoning, LLMs analyze the query and make informed decisions. 

Query planning agents

Query planning agents break down complex queries into manageable sub-queries or inputs and handle them to agents with access to specific RAG pipelines. Later, query planning agents consolidate all responses into a comprehensive response to solve the user’s request. 

ReAct (Reasoning and Action) agents 

Using real-time data and user interactions, this agent type leverages reasoning from multiple components and iterative actions to address more complex queries. To apply this approach, ReAct agents integrate routing, query planning, and tool use into a single workflow to handle multi-step, sequential queries while ensuring context. 

Dynamic planning and execution agents 

This agent model optimizes and adapts to changes in the real-time environment and data requirements. Using its planner and executor components, dynamic planning and execution agents first plan a step-by-step guide for a query; then, the executor performs each step. This iterative process continues until each plan is executed for the final delivery of the task. 

Multi-agent systems 

As the name appears, this model combines multiple agents with specific skills and knowledge to address complex queries within the RAG framework. 

AI copilot exhibits a multi-agent framework to enhance information retrieval and generation for different problems.

The inherent capabilities of agentic RAG to solve more complex and nuanced problems allow industry leaders to unlock immense potential for business growth by cutting costs and building long-term customer retention. Here are some great benefits of agentic RAG.

Cost efficiency 

Agentic RAG is an excellent tool for inspiring enthusiasts to become AI adopters. Unlike traditional tools, agentic RAG optimizes retrieval data and avoids rerunning retrieval while generating a response. This reduces consumption rates and saves companies money as they aim to build AI-first customer or employee support.

Additionally, agentic RAG frees agents to focus on more strategic business needs and saves costs by surfacing accurate, relevant, and context-aware responses without human interventions. 

Hassle-free scaling

Agent-based RAG systems, or agentic RAG, follow a modular design. It is easy for developers to integrate a new system or new data sources when a company grows without affecting the functioning of the entire system. Scaling new functionalities is seamless while providing answers in accurate form with the expanded knowledge base. 

Enhanced user experience 

Agentic RAG enhances the user experiences of customers or employees by delivering accurate and relevant responses. Not only this, responses are faster, personalized, and seamless. 

Agentic RAG efficiently addresses the limitations of traditional RAGs, opening up opportunities for customer-facing ladders to manage complex queries and boost user experiences.

Industry leaders enthusiastic about transforming the way they manage enterprise workflows can leverage agentic RAG in many ways to boost efficiency and productivity. Using agentic RAG in their key workflows or systems allows them to speed up response by pulling information from various sources rather than a few and reduce response time, resulting in more engaging connectivity and positive business results. 

• Employee support: Agentic RAG ensures all departments leverage accurate and relevant responses to solve problems and execute tasks. Not only these, but employees can also handle multi-step workflows independently using Agentic RAG. For example, an employee support agentic RAG bot identifies an employee’s issue that has been unattended for many days by looking at chat history and some documents. From there, the bot understands the intent of the query, investigates it further, and provides real-time resolutions in one go.  

• Customer support: Customer queries are dynamic, which Agentic RAG bot can handle seamlessly with the ability to reason, plan, and act. For example, a customer’s troubleshooting queries for a purchased camera involve many steps.  An agentic RAG system or chatbot breaks down the problem into small chunks of steps, referring to additional information resources and retrieving relevant information to provide more accurate and meaningful troubleshooting guides. 

• Seamless knowledge management: Being able to fetch information from a variety of third-party or external sources and analyze their relevance, Agentic RAG systems help users access crucial information and make informed decisions for any particular task. 

• AI copilots: Agentic RAG-powered AI copilots are AI assistants that help users execute multiple tasks, such as summarizing, translating, and synthesizing information.  For example, AI copilots can reason over vast amounts of data, identify patient care and e-commerce shopping recommendations, and generate financial advice, among many other tasks.

Leveraging agentic RAG for enterprise workflows improves information retrieval and generation functionalities. As it improves decision-making and workflow automation, industry leaders can unlock immense possibilities from agentic RAG. However, agentic RAG implementation needs a careful approach for various reasons. Several key steps include, 

1. Define objectives: Set clear objectives for achieving specific goals, such as improving response across chatbots, content generation, etc.   

2. Select tools: The retrieval and generation systems in the RAG framework require appropriate document fetching and response generation tools. Determine which tools may best suit your business needs and select them. 

3. Build a team: It is essential to have a skilled team with adequate AI knowledge and expertise. It is also crucial to allocate enough resources for development, deployment, and testing. 

4. Prepare data: Gather essential documents to capture data for preprocessing, cleaning, tokenizing, and normalizing for compatibility. 

5. Build the retrieval devices: Index the collected documents and convert user data into retrievable formats. 

6. Integrate RAG systems: Send queries to the retrieval system by fetching relevant documents and then pass retrieval information through the generation systems to produce context-rich outputs. 

7. Fine-tune the agentic RAG model: It is time to train and fine-tune the GenAI models with datasets consisting of paired queries and contextually aware responses. Continuous evaluation and a feedback loop of the model are essential to monitor and maintain changes as per LLM evolutions.

8. Deploy the model of agentic RAG: Integrate with APIs to combine with external systems and improve context-aware response delivery. Alongside this, monitor system performance to minimize model risks.

Some potential challenges may arise as you look forward to implementing agentic RAG workflows for your enterprise systems. Be careful and consider these implementation challenges. 

1. Data quality and availability: Agentic RAG workflows must integrate with high-quality data to produce accurate, complete, and relevant output. Conversely, poor data quality can lead to unreliable responses. Implementing robust data management and quality assurance is essential. 

2. Interpretability and explainability: AI is known to follow a black-box theory, meaning its reasoning and decision-making process are unknown. While developing models and systems, ensure your agentic RAG systems can explain their reasoning and data sources to establish trustworthiness and accountability.  

3. Privacy and security concerns: AI and LLMs are prone to hallucinations and privacy infringement, among other threats. Guardrails such as access controls and data protection measures should be implemented to safeguard privacy and prevent data breaches.

Several well-known frameworks for implementing agentic RAG models and enhancing information retrieval and generation. Many of the frameworks that help with the implementation of agentic RAG models include, 

LlamaIndex: LlamaIndex introduces the QueryEngineTool, a powerful foundation tool for building agentic RAG systems, which gives access to a collection of templates for retrieval tools. 

LangChain : LangChain allows users to employ and enhance chain-of-thought processing. It provides many frameworks, such as LCEL and LangGraph, for developing retrieval and generation systems over large language models. 

CrewAI : If you aim to build a multi-agent system, CrewAI is one of the best frameworks. These tools are best used to achieve complex goals. 

Many LLM frameworks offer diverse capabilities for employing agentic RAG models with specific features. You can pick the one that meets your requirements, such as integrations and scalability needs.

Agentic RAG is the leap forward. It is evolving at scale and is aided by new technologies like LLMs to expand use cases for enterprise workflows. Here’s what’s ahead. 

1. Multimodal data integration: RAG systems will be more robust in connecting multiple data resources such as texts, images, audio, video, and more. 

2. Multi-lingual capacity: Agentic RAG will enhance its multilingual capabilities, breaking language barriers and expanding global acceptance. 

3. Advanced NLP processing: Innovations and advancements in natural language processing are opening up opportunities to help resolve more nuanced queries and problems in customer and employee support. 

4. Stringent focus on explainability: With time, these RAG systems will become more complex, leaving room for innovators to focus on explainability regarding how these systems process decision-making.

Workativ's conversational AI or LLM-powered chatbot uses a powerful agentic RAG system for employee support. By unleashing powerful information retrieval and generation processes, Workativ helps enterprise leaders automate complex tasks and unlock massive benefits for productivity, efficiency, and business growth. 

Workativ’s Knowledge AI exhibits agentic RAG capabilities, allowing your business to handle more complex queries, free up agents, and save money. 

1. Generative AI or LLM capabilities: Knowledge AI builds on large language models to generate relevant, context-aware responses. 

2. Specific knowledge integration: Knowledge AI provides access to connectors or APIs to integrate with third-party resources and enhance intent and context understanding. This approach helps fetch accurate and domain-specific answers. 

With Knowledge AI, our agentic RAG systems, users can address the limitations of traditional RAG systems and overcome the challenges of complex enterprise workflows. Here’s how Knowledge AI unleashes many advantages for your enterprise problems. 

1. Capture accurate and context-aware information: Knowledge AI allows you to fetch data from multiple systems and reduce time to achieve a task goal. 

2. Enhance user experience: Knowledge AI improves enterprise search and information discovery with its agentic RAG capabilities, ensuring users can handle their problems independently and agents can focus more on strategic issues. 

3. Streamlined enterprise workflows: Knowledge AI allows service desk managers to easily automate customer or employee support tasks, reducing the steps required to handle manual and repetitive tasks.

Agentic RAG is the future with evolving changes on the AI side. Enterprises can wish to address the limitations of scarce knowledge and gain access to more precise and accurate information to accomplish more tasks and increase employee productivity.  

Workativ is paving the way for AI enthusiasts to seamlessly capture the advantages of advanced AI tools such as agentic RAG through their LLM-powered conversational AI bots. It is a no-code platform that offers features to translate the tool into agentic RAG capabilities. By providing interoperability, scalability, and enterprise integrations, Workativ allows you to unlock new opportunities and unleash the full potential of agentic RAG for better business outcomes. 

Schedule a demo today. 

What is Agentic RAG?

AI agent implementation on the RAG pipeline is known as agentic RAG to optimize and manage retrieval and generation processes better than traditional RAG. Using intelligent agents within the traditional RAG pipeline improves reasoning, analysis, and information generation, resulting in better response outcomes. 

How does Agentic RAG enhance the user experience?

Agentic RAG enhances the rate of context-aware responses for user queries by boosting the integration of tools with third-party resources. With advanced reasoning, agentic RAG can improve faster response time, deliver more relevant and accurate answers, and offer personalized responses. 

How is Agentic RAG different from traditional RAG?

Traditional RAG works with predefined scenarios, and there are chances of hallucinations due to a lack of real-time information on abundance. On the other hand, Agentic RAG uses many AI agents that help boost reasoning and drive straightforward information retrieval and generation by breaking down complex queries into sub-queries and then validating them to deliver more accurate responses. 

What are the use cases of Agentic RAG?

Agentic RAG has multiple uses for industry leaders. It can be better used for customer and employee support chatbots, summary and content generation, fraud analytics, knowledge management, and AI copilots for finance and marketing teams. 

How does Agentic RAG ensure improved retrieval and generation?

When exposed to new and unique data, intelligent agents in agentic RAG learn and evolve over time, expanding their external knowledge sources and gaining additional abilities to address complex user problems. Agentic RAG’s memory system continuously monitors previous interaction history and ongoing communication pathways to become more intelligent and stay relevant to the present scenarios.