Agentic RAG vs RAG: Key Differences and Practical Applications

Traditional RAG systems have given businesses decent results, but they're hitting their limits. They have limited retrieval abilities and are not dynamic. These gaps are now filled through Agentic RAG.

Agentic RAG systems can go through multiple retrieval processes and deliver on AI's promise of autonomous problem-solving.

In this blog, let's briefly explain what Agentic RAG is, what are key differences between Agentic RAG and RAG, and how they are becoming the epicenter of modern-day AI agent development. 
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What Is RAG?

Retrieval Augmented Generation (RAG) is an AI framework, designed to improve the baseline performance of LLMs or Large Language Models. How? By integrating external information retrieval mechanisms.

Generally, LLMs combine retrieval and generation functionalities to provide context-specific information about a given dataset. This leads to limited accessibility to the data. The classic example of this is the earlier versions of ChatGPT that were only able to provide you information up to 2023 because the underlined LLM modal has static understanding.

RAG bridges this gap by enabling LLMs to connect with external data sources so that they can pull or retrieve relevant information to improve contextual understanding.

Through this, RAG solves a fundamental problem with large language models: they don’t know what they don’t know. This approach is particularly valuable for businesses because it allows AI systems to access the latest information without retraining the entire model.

The customer support chatbots that most businesses use to address customer queries are built using RAGs. They are connected to the company’s knowledge base, giving them the ability to retrieve accurate information about the company’s products, policies, and customers. This makes it possible for AI chatbots to provide faster and accurate responses to users as per their query.
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Challenges of Traditional Retrieval Augmented Generation

While it has advantages such as dynamic updates and domain-specific understanding, traditional RAGs methods are running into roadblocks, particularly in handling complex queries, presenting multiple challenges.

1. One-and-done Retrieval: Traditional RAG performs a single retrieval at the beginning and then never comes back. While this approach works for simple problems, it fails to perform in complex scenarios that require multiple information types.
2. Limited Context Space: The LLM’s context window can only retrieve limited information. When dealing with multiple product specifications, customer histories, or regulatory documents, traditional RAG must either exclude crucial information or become prohibitively expensive.
3. No Strategic Thinking: These systems don’t care what information they need. They retrieve based on similarity, not relevance to solving the specific business problem.
4. Poor Error Recovery: If the initial retrieval misses critical information, traditional RAG has no mechanism to recover it. It will continue performing without retrieving the missed information. Because of this, businesses often experience inaccurate decision-making.
5. Domain Constraints: RAGs are domain-specific. They will only work on the domain of data they are retrieving. For instance, if the RAG is using marketing data, it will perform poorly when asked any legal or financial question. Hence, they can’t multi-task.
6. Convincing but Wrong Answers: When relevant information isn’t retrieved, traditional RAGs tend to generate plausible-sounding but incorrect responses. This makes them irrelevant in situations where decisions carry real financial consequences.

In practice, these limitations mean that many organizations implement RAG only to discover that their systems need ongoing human monitoring, defeating much of the anticipated gain in efficiency.
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What Is Agentic RAG?

Next, we have Agentic RAG, an advanced AI framework built over traditional RAG but has cut-above capabilities. In Agent RAG, the conventional RAG is powered by fully autonomous and highly intelligent AI agents.

These agents can learn from their environment, update their memory based on the new learnings, and have greater reasoning abilities. They also determine which data resource should be used to collect the relevant information to resolve the given queries, making Agentic RAGs capable of handling complex and multi-tasking scenarios through a sophisticated RAG pipeline.

Introducing multiple agents into the system allows for specialization in distinct tasks, such as data retrieval from various sources, thus improving overall efficiency and performance.

Learn more about Agentic RAG here. 

Unlike traditional RAG where retrieval takes place only once and is then forgotten forever, Agent RAG retrieves continuously, leading to high dynamic context awareness.

Wondering what all AI agents can do for your enterprise? Read this.

At its essence, Agentic RAG adds an “agent” layer between users and standard RAG elements.

• These systems investigate business questions more deeply.
• They find and bridge any knowledge gaps.
• Facilities like calculators or APIs aid the process when needed.
• The precise information needs of each segment inform the strategy.
• It uses suitable methods for collecting information.
• Accuracy is always a concern during the process.
• In-depth and wide-ranging research improves the quality of answers.

Do you know that by adopting agentic RAG, enterprises from manufacturing industries are able to reduce quality-related issues by 25%?
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Benefits of Agentic RAG

The advantages of Agentic RAG translate directly to improved operational effectiveness: by leveraging contextual data and user intent, these systems provide contextually relevant responses that are more comprehensive, accurate, and informative than traditional methods reliant on static queries.

Moreover, the integration of retrieved data into coherent, meaningful outputs is crucial for the effectiveness of these systems. Traditional RAG processes often struggle to contextualize the retrieved information, leading to inadequate responses. However, sophisticated mechanisms in Agentic RAG systems address these challenges, ensuring that the data presented is both relevant and useful.

Handling Business Complexity

Agentic RAG systems can deal with complex business and user queries that are beyond the scope of humans or traditional RAGs. For instance, suppose a board member requests information on market expansion possibilities, the system doesn’t simply pull in generic market reports.

It will recognize relevant elements, aggregate specific regional information, compare competitive situations and combine fiscal projections, which the traditional RAG and LLMs won’t be able to do.

Progressive Problem-Solving

Agentic RAG systems can tease problems step by step, just like your strategy team would. Instead of working on the problem as a whole, Agentic RAGs will break it down into smaller sub-tasks and then work on it.

Each problem-solving stage is based on previous findings and the refinement of the user query, enabling advanced analysis that routine RAG can’t accommodate.

Focused Information Use

Rather than wading through all potentially relevant documents while making decision-making, Agentic RAG only captures what’s required. This leads to more accurate answers at lower computational costs.

Self-Correcting Capability

When data is in conflict or incomplete, Agentic RAG systems can determine the problem and look for additional sources. This leads to accurate and reliable information synthesis and reduces the need for manual data verification. 

Extended Capabilities

Agentic RAG can include specialized functionalities such as financial calculators, forecasting algorithms, or industry APIs. This makes Agentic RAG have extended capabilities compared to traditional RAG and LLMs and makes them more sophisticated in analysis and workflow automation. 

Improved Decision Support

By integrating strategic retrieval with reasoning, Agentic RAG systems generate more precise, relevant, and complete analyses supporting business decision-making.

Transparent Reasoning

These systems can explain their thinking process, showing executives how we reach conclusions and what information sources we consult. It is essential for building trust in high-stakes business contexts.

This translates to AI systems that ultimately fulfill the vision of minimizing human intervention while enhancing output quality.

Explore how Agentic workflows are changing the AI landscape for businesses. 

How To Apply Agentic RAG In Your Enterprise

As Agentic RAG is advanced, has higher reasoning capabilities, and performs retrieval continuously, enterprises have higher possibilities to integrate it through optimized rag pipelines.

Here is a structured approach to integrate Agentic RAG in your enterprise.

1. Define Use Cases & Objectives

Start with identifying the high scenarios to implement Agentic RAG. The most common use cases are customer support automation, legal document analysis, and market research. 

2. Select Required Components 

Next, assemble requirement components such as retrieval systems such as  BM25 or dense vector databases (e.g., FAISS) for semantic search, generative models such as GPT- 4 & Claude, and agent frameworks such as LangChain or CrewAI. 

3. Prepare Data Infrastructure 

You need to aggregate structured and unstructured data for the training and learning of the Agentic RAG. Make sure the data is clear and preprocessed to align well with retrieval and generation requirements. 

4. Design the AI Agents Architecture

Enterprises next have to determine which type of agent they need for specific use cases. For this, they need a reasoning agent or an action agent. Based on the type, they have to define how the agents will communicate and exchange information with each other.

Next comes logic designing which involves allowing agents to plan their steps and decide which relevant data they should retrieve.

The agent architecture step finishes by determining how the Agentic RAG will maintain context and track the process.

5. Implement The Agentic RAG Components 

Develop pipelines for data ingestion, implement the process of data conversion, index the selected vector database for fast retrieval of retrieved data, and implement the logic.

6. Integrate with Enterprise Systems 

The Agentic RAG is then integrated with the enterprise systems through APIs. Post integration, embed the Agentic RAG capabilities into existing business workflows to empower its efficiency and decision-making abilities. 

7. Implement Security and Governance

To avoid security concerns, implement security and governance measures such as access control, data privacy, monitoring, bias detection, auditing, and so on. 

8. Test and Iterate

Test the efficiency of the Agentic RAG through extensive testing that involves using a wide range of test cases to assess the response accuracy and conducting UAT or User Acceptance Testing. 

Once testing is done, iterate on the system’s design, performance, data, memory, and implementation based on the testing results and user feedback. 

9. Plan for Scaling and Maintenance

Pay attention to the infrastructure requirements for the scaling of your Agentic RAG system. In addition, deploy adequate measures to fine-tune LLMs and other embedded models. 

10. Train and Support Users

Lastly, clear training materials should be created to help end-users understand how the Agentic RAG works. In addition, set up different support channels to provide ongoing support. 

Applications & Use Cases of Agentic RAG in Business

Agentic RAG is already creating its mark across diverse areas of business through enhanced user interactions. And, when it comes to its applications & use cases, options are endless.

Customer Support: Enterprises can integrate Agent RAG to develop AI agents to handle customer inquiries. They can help customers solve feature-related queries, learn about the service prices, and even recommend a relevant upgrade.

Market Intelligence: Executives and businesses can build AI agents with agentic RAG for market research and obtain distilled intelligence from market reports, competitive announcements, regulatory updates, and internal sales statistics.

Instead of pulling identical documents, the system can pick up on burgeoning trends, estimate potential effects, and point out strategic opportunities specific to your enterprise environment.

Legal Contract Analysis: Legal teams can accelerate contract review by having systems that understand legal frameworks, identify non-standard clauses, retrieve relevant precedents, and flag potential risks. The agent can navigate the complicated contracts section by section, comparing terms to master templates and corporate policies.

Financial Modeling: Financial analysts can build automated data pull AI agents with Agentic RAG to establish market context. An Agentic RAG here can draw appropriate market size data, calculate projected returns based on specific company drivers, and account for qualitative risk.

Healthcare Provider Assistance: Clinicians can use Agentic RAG for medical literature, patient histories, treatment protocols, and insurance mandates to assist with diagnosis and treatment planning.

Supply Chain Optimization: Operations leaders can use Agentic RAG systems to measure inventory positions, supplier issues, logistics limitations, and forecasts.

Product Development: R&D teams can pace innovation with Agentic RAG systems as they can help teams extract technical specifications, patent data, market needs, and material characteristics. AI agents built with Agentic RAGs can detect potential design conflicts, recommend alternative methods, and assess manufacturability constraints.

Regulatory Compliance: Compliance officers can navigate complex regulatory environments more effectively with Agentic RAG systems that track changing requirements, assess their impact on specific business processes, and recommend implementation approaches.

Each application delivers tangible business value by solving previously too complex problems for traditional AI approaches.

Difference Between Agentic RAG and RAG 

The differences between retrieval augmented generation rag (RAG) and Agentic RAG are visible and striking. Traditional RAG passively fetches information when asked. Agentic RAG actively thinks about what information it needs, goes and gets it, evaluates its quality, and decides what to do next, just like your best employees.

However, that’s not what RAG and Agentic RAG means. It extends to the way they both handle queries, demands initial investments, and so on.

CEOs and CTOs investing strategically in AI must learn about Agent RAG vs RAG to ensure that they have the right system in place.

Conclusion

The differences between RAG and Agentic RAG is more than technical. It shows different approaches to AI implementation and value creation.

Traditional RAG systems have delivered significant improvements over pure LLMs, but their limitations are becoming increasingly apparent as businesses seek more sophisticated AI capabilities. The static, one-size-fits-all approach to information retrieval works for simple use cases but breaks down when faced with the complexity of real business problems.

Looking to build RAG based AI solutions for your business?

Contact our LLM and AI experts today!

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