What is Agentic RAG and how does a RAG agent work?

In a world where speed, accuracy, and trust are everything, businesses are turning to advanced AI to power customer conversations and automate decisions. But not every answer that AI agents generate is created equal. The difference between a helpful automated assistant and a frustrating experience often comes down to one thing: access to real, up-to-date business knowledge. This is where Agentic RAG and the concept of the RAG agent come into play.

A visual layout shows four individuals using their phones, each representing a failure mode of AI assistant responses. The first person, a man, receives an irrelevant response about a bank CEO when asking for a branch location, labeled as "Context Misunderstanding," leading to "Increased Frustration" with outcomes like "Costly human escalation" and "Lower CSAT." The second, a woman, asks about placing a bulk order, but receives incorrect procedural information, labeled "Procedure Error," resulting in "Slow Task Completion" and "Missed revenue opportunities." The third, another woman, asks about taking Ibuprofen with her prescription and receives dangerous misinformation, labeled "Factual Inaccuracy," with risks like "Legal risk" and "Reputational damage." The fourth, a man, asks about flight upgrade options and gets a misleading overgeneralized answer, labeled "Overgeneralization," leading to "Loss of Trust" and outcomes like "Churned base." Each error is marked with a red warning icon, and consequences are summarized beneath each case.

What is Agentic RAG?

Agentic RAG refers to the integration of AI agents into retrieval-augmented generation (RAG) systems. By adding AI agents to the RAG pipeline, these systems become more adaptable and accurate.

Unlike traditional RAG systems, agentic RAG connects a generative AI model with an external knowledge base that enables large language models (LLMs) to retrieve information from multiple sources and manage more complex tasks and workflows.

The result? Customers and employees get trustworthy, business-specific answers delivered by smart RAG AI agents.

What is a RAG agent?

A RAG agent is an intelligent AI assistant designed to retrieve, understand, and synthesize the most relevant information from a wealth of data resources.

Rather than relying on guesswork or pre-programmed answers, a RAG agent searches your actual company content, FAQs, policies, manuals, or knowledge bases, and generates context-aware, grounded responses. This minimizes misinformation and hallucination, making every interaction more reliable and aligned with brand standards.

How does Agentic RAG work?

The power of agentic RAG comes from a robust, multi-step process that transforms messy enterprise documentation into instant, actionable answers:

Ingestion: The system securely ingests all your business documentation PDFs, knowledge bases, and manuals.
Preprocessing & chunking: Content is cleaned and split into manageable, context-rich “chunks” to optimize search.
Embedding & indexing: Each chunk is turned into a searchable vector format, ready for rapid retrieval by the RAG agent.
Retrieval: When a user asks a question, the rag agent quickly identifies and pulls the most relevant content chunks.
Generation: The Agentic RAG system then generates an answer rooted in this retrieved data, ensuring every response is accurate and relevant.
Action optional: In advanced applications, RAG agents don’t just answer, they might trigger workflows, delegate requests, or connect to other business tools, acting with real-world purpose.

Why agentic RAG and RAG agents matter

Today, most AI chatbots can’t keep up with the dynamic, granular needs of business users. Generic AI models can hallucinate, misread policies, or miss the fine print. This puts customer trust, compliance, and brand reputation at risk.

Agentic RAG bridges this gap by ensuring AI responses are tightly grounded in real, authoritative content. With rag AI agents, you can count on each conversation to be accurate, up-to-date, and aligned with your latest business standards. This leads to:

Fewer escalations to human agents
Faster resolutions and improved customer satisfaction
Stronger brand loyalty and trust

Key use cases for agentic RAG and RAG agents

Agentic RAG and rag agents can transform enterprise workflows across multiple industries and channels. Here are a few use case examples to say the least:

Customer support

Instantly answer product, policy, or troubleshooting questions using the exact language from your own documents.

Banking & financial services

Provide fast, compliant responses based on current regulatory documentation and customer history.

Retail & eCommerce

Resolve refund, shipping, and product inquiries with precise, policy-aligned answers.

Human Resources

Explain policies, benefits, and procedures to employees with clarity and accuracy.

Omnichannel service

Deploy rag AI agents seamlessly across channels like WhatsApp, RCS, Viber, Messenger, and in-chat apps, and meet your customers wherever they are.

What makes Infobip’s Agentic RAG pipeline unique?

We knew early on that RAG was our best bet for delivering contextual, accurate AI responses in real time. But to make that work at scale across WhatsApp, RCS, Messenger, Live Chat, and more, we had to build RAG into the fabric of our stack.

Building an effective Agentic RAG system isn’t easy. Many organizations underestimate the engineering and data management required. That’s why we have developed an enterprise-class agentic RAG pipeline built from the ground up for accuracy, scale, and ease of use:

Document-first indexing: Handles all types of unstructured documentation, regardless of format or “hygiene”.
Custom chunk reordering: Prioritizes contextually relevant data, minimizing unnecessary back-and-forth.
Multi-retriever architecture: Combines keyword, vector, and hybrid search so RAG agents always surface the best sources.
Query rewriting: Refines ambiguous user questions, ensuring RAG agents fetch better responses.
Response scoring & hallucination management: Every answer is checked for factual accuracy, and non-critical queries are cleverly handled.
Seamless content updates: Upload new documents and the underlying AI knowledge base auto-updates, ensuring answers keep pace with changing policies or information.
Modular deployment: Scale from targeted, single-channel deployments to full omnichannel agentic RAG orchestration.

Getting started with Agentic RAG and RAG AI agents

Deploying an agentic RAG system is smoother than you think. With Infobip’s managed platform, you don’t need to hire extra engineers or worry about infrastructure. Simply provide your documentation and some basic structure, and we’ll handle ingestion, tuning, and ongoing monitoring.

Choose the deployment tier that suits your business—higher-accuracy agentic RAG for complex, compliance-heavy domains, or lightweight RAG agents for lower-stakes use cases. Either way, you’ll see faster go-live, less manual effort, and more reliable results than traditional approaches.

The future of AI is agentic

As businesses look to deliver conversational AI that’s truly helpful, compliant, and engaging, Agentic RAG and RAG agents are setting a new industry standard.

The next wave of conversational AI will be won on performance, not hype. And for that, RAG remains undefeated. So if you’re tired of hallucinations, disappointed by generic chatbots, and struggling to bring LLMs into production, talk to us. You bring the docs. We’ll handle the rest.

Ready to see how agentic RAG can accelerate your business?

Get in touch to learn more about our end-to-end agentic RAG pipeline and schedule a demo.

Contact an expert