Introduction 

One of the major themes highlighted at the recent 2025 CES conference was the role of agentic AI and the future of autonomous AI solutions to revolutionize decision-making processes. During his keynote, Nvidia CEO Jensen Huang emphasized the transformative power of AI agents in bridging human creativity with machine precision. The emergence of agentic AI marks a new phase in the automation of complex processes and the sophistication of tailored user interactions. 

The proliferation of Large Language Models (LLMs) over the past decade has been making the lives of data experts easier and workflows more efficient. With the integration of Retrieval-Augmented Generation (RAG), the quality and accuracy of these LLMs have been significantly improved, providing contextually aware outputs with domain-specific queries. With the help of AI agents, these systems are evolving to a new layer of adaptability and autonomy. Unlike most generative AI, which responds to inquiries with static content and predefined outputs, agentic AI relies on intelligent agents’ capabilities to autonomously plan, execute, and adapt to tasks, often across multiple steps, without constant human intervention.  

For industry leaders, this not only signals a paradigm shift in how technology interacts with humans but also opens new doors to applications that can execute multi-step, self-directed tasks. 

RAG Fundamentals: A Refresher 

Now, the concept of Retrieval-Augmented Generation (RAG) is probably not new to you. The framework essentially encapsulates three key components, including query analysis, information retrieval, and response generation. With its unique capability to integrate real-time, contextually relevant external knowledge, RAG has become a paramount part of enhancing LLM performances through the integration of external knowledge sources into its reasoning process. 

Here’s a graph to help you visualize the process of RAG: 

When the retriever receives a prompt, it searches the targeted knowledge bases and identifies relevant information before retrieving documents and data to feed back to the users. Once the information has been retrieved, the LLM model then combines the data and initial user query to create a coherent response.  

Since most LLMs are trained on vast volumes of data and use billions of parameters to generate outputs in response to different queries, the quality of the training data becomes paramount in determining the performance of the output. Ultimately, RAG systems improve the quality of LLM outputs by incorporating real-time knowledge bases. 

To explore the transformative potential of Retrieval-Augmented Generation (RAG) systems in enterprise knowledge management, check out our comprehensive paper on how to build an enterprise knowledge base using vector databases and LLMs here. 

AI Agents: The Future of Autonomous Decision-Making  

Agentic AI is a technology that was conceptualized during the rise of deep learning and became more prominent when the capabilities of neural networks began to surpass traditional AI methods. Industry leaders such as Nvidia have placed a significant focus on the development and optimization of agentic AI, stating that they are set to revolutionize the workforce. 

AI agents are equipped with the ability to execute self-contained tasks with minimum to no human intervention. This means that these models can generate leads and follow up on tasks without receiving requests from a query. For example, when an AI agent detects a potential customer’s increased page retention time on a platform, it can proactively send personalized recommendations based on the webpage he’s viewing, schedule follow-up emails, or even initiate a conversation through chatbots to nurture the lead. 

These agents leverage real-time data to detect anomalies within the datasets, and implement corresponding measures without manual intervention so that the operational workflow is significantly improved as they are proactively bridging the gap between detection and response. 

Check out our comprehensive white paper on AI agents to learn more about how they are transforming the way businesses utilize AI. 

Agentic RAG: When AI Takes Initiative

Now, to combine the optimized output performance achieved by the RAG system and the autonomous decision-making capability of agentic AI will unlock a new paradigm in AI-driven solutions, enabling systems to deliver more context-aware, efficient, and self-sustaining operations. 

Enter Agentic RAG: an agent-based implementation that is not only capable of retrieving relevant information in real-time but also determining which tasks to execute without human intervention. 

With agentic RAG, the augmented system is now capable of orchestrating multi-step reasoning and dynamically refining its outputs. Since traditional RAG primarily focuses on retrieving and generating responses in a single step, agentic RAG is like an intelligent assistant that not only retrieves data but also interprets, validates, and iterates to ensure the response aligns with complex user requests. 

Here are some of the key differences between traditional RAG systems and agentic RAG systems:

Agentic RAG Integration 

Agentic RAG systems are powerful, but when it comes to putting things together, integrating information retrieval with autonomous decision-making requires careful consideration. Companies looking to leverage the full potential of an Agent RAG system need to combine the RAG model with agentic capabilities to improve responsiveness. As such, the integration of agentic RAG requires developing a hybrid model that integrates information retrieval and generation with decision making in a fully autonomous manner. 

Step 1 

To start with, you need a RAG model that can retrieve relevant documents from a knowledge base. This is often achieved through embeddings, where data is encoded for relevance. Once the information is retrieved, a generation model should be there to generate appropriate outputs for the users.

Step 2 

Next, the agentic layer. In this phase, the model can make decisions about which actions to take based on the tasks given. Since the system is agentic, meaning that they can perform tasks autonomously, they will likely initiate tasks, interact with APIs, or directly respond to user queries depending on its objectives. 

Step 3 

The final step is fine-tuning the system based on each output performance. This could involve the process of evaluating model performance and adjusting its parameters to improve the accuracy of both retrieval and generation tasks. Additionally, continuous monitoring and user feedback integration are also crucial to refine the agentic decisions the model makes. 

The process of integration and deployment of an agentic RAG system can be complex, especially for small-scale companies starting to incorporate advanced AI capabilities into their existing infrastructure. This is where Shakudo comes in—transforming a complex process into a streamlined experience. 

Our comprehensive platform currently features over 170 integrated data tools, making it an all-in-one solution for building, deploying, and managing machine learning models. As an operating system, we ensure dynamic resource allocation and scalability, allowing for efficient performance as demands grow. The platform enables efficient data retrieval through advanced query mechanisms, supports fine-tuning of pre-trained models for tailored responses, and orchestrates multiple models to integrate autonomous decision-making. 

With our team of experts handling the technical complexities of integration, deployment, and ongoing optimization, business leaders can focus on driving growth while leaving the heavy lifting of AI system management to us.