Glossary:Agentic RAG
9 min.
What is Agentic RAG?
Agentic RAG (Agentic Retrieval-Augmented Generation) is an AI architecture where an autonomous AI agent independently decides which sources of knowledge to query, in what order to combine information, and how to connect multiple retrieval steps to construct a complete answer.
The key difference from traditional RAG: In Basic RAG, the system follows a fixed process — input in, search once, answer out. Agentic RAG, on the other hand, makes independent decisions. The agent evaluates interim results, selectively chooses from multiple sources, and initiates additional searches as needed until the answer is complete and reliable. This turns a reactive retrieval system into a true digital advisor.
40 million AI-assisted sessions have already been processed by branchly — a benchmark that shows how powerful agent-based retrieval strategies are in practice (Source: branchly, 2026).
How does Agentic RAG work?
While traditional RAG operates like a simple database query, Agentic RAG behaves more like an experienced researcher: It plans, checks, asks, and combines.
The process in practice:
Analyze and decompose the request: The agent understands the actual intent behind the user question. A question like "Which savings model suits me if I want to buy an apartment in three years?" is broken down into several sub-questions.
Develop a source strategy: The agent decides which sources of knowledge are relevant — product catalogs, FAQ databases, website content, external database connections — and in what order to query them.
Iterative retrieval: After each retrieval, the agent evaluates whether the information found is sufficient or whether further searching is necessary. It can formulate follow-up questions internally and start new queries without user intervention.
Merge results: The information from multiple sources and retrieval iterations is combined into a coherent, complete answer.
Generate and validate the answer: The Large Language Model (LLM) formulates the final answer — based on verified, sourced content rather than on training hallucinations.
At branchly, the branchlyAI Engine takes on precisely this agent-based retrieval process. It does not just query a single source but autonomously searches website content, product catalogs, FAQ databases, and connected data sources — and merges the results into a precise, context-aware answer.
Agentic RAG vs. traditional RAG
Feature | Traditional RAG | Agentic RAG |
|---|---|---|
Retrieval strategy | One query, one source | Multiple queries, multiple sources — autonomously planned |
Decision logic | Fixed, predefined process | AI agent dynamically decides next steps |
Handling of interim results | No evaluation — direct answer | Agent evaluates and decides whether further search is needed |
Source selection | Predefined by system configuration | Agent independently selects the most relevant sources |
Complex queries | Weak — often incomplete answers | Strong — breaks complex questions into sub-steps |
Hallucination risk | Reduced compared to pure LLM | Further reduced through multi-level verification |
Maintenance effort | High — each new source must be explicitly integrated | Lower — the agent adapts to new sources |
Answer quality | Good for simple, direct questions | Very good even for complex, context-dependent questions |
Forrester and Gartner consistently describe Agentic RAG as the next maturity level of retrieval systems — from tool to independently acting knowledge mediator.
Why Agentic RAG makes a difference
Targeted reduction of hallucinations
The fundamental problem with large language models (LLMs) is their tendency to generate plausible-sounding but factually incorrect answers. Traditional RAG already significantly reduces this — but Agentic RAG goes further. Through iterative verification and cross-checking across multiple sources, the error rate is further lowered.
According to an analysis by Vectara (HHEM Benchmark), RAG-based architecture reduces hallucinations by 40 to 71% compared to pure LLM responses (Source: Appinventiv, 2025). Agentic RAG improves this value again through multi-level retrieval chains.
A practical example from the financial sector
Morgan Stanley has employed Agentic RAG for its advisor platform. The result: The acceptance rate among advisors rose to 98%, and the accuracy of document retrieval improved from 20% to 80% (Source: ArticleSledge, 2025). This shows that Agentic RAG is not only technically superior but also produces measurable business results in practice.
A growth market with clear direction
The global RAG market is rapidly developing. MarketsandMarkets estimates the market at $1.94 billion (2025) with growth to $9.86 billion by 2030 — a CAGR of 38.4% (Source: GlobeNewswire, Nov. 2025). The Business Research Company even predicts growth from $2.11 billion (2025) to $11.55 billion by 2030, with a CAGR of 41% (Source: GII Research, March 2026).
Agentic RAG is the driver: Companies that currently rely on autonomous multi-source retrieval architectures are building a technological lead that will be difficult to catch up with later using simple single-source setups.
Agentic RAG at branchly: How the branchlyAI Engine implements it
branchly does not use Agentic RAG as a buzzword, but as operational infrastructure. The branchlyAI Engine independently decides which sources of knowledge to query for each user request: website content, product catalogs, connected FAQ databases, external data — depending on the question, in various combinations and sequences.
This makes a noticeable difference for visitors: They do not receive generic standard answers but context-aware, complete information — regardless of how complex or multi-faceted their question is. branchly chatbots achieve an interaction rate of 5 to 10% as a widget (compared to the 0.5 to 1% industry average) and even 45 to 50% as an embedded interface (Source: branchly customer data, 2026). These numbers show what happens when Agentic RAG is implemented correctly: users notice that the system truly understands their questions.
branchly has already served over 11 million users — on websites that natively cover 101 languages, without the need for translated content. The branchlyAI Engine understands and answers inquiries in the visitor's language, regardless of the source language of the website.
branchly Module: branchlyAI Engine (Multi-Source Retrieval) — available from €499/month (Starter, 1,000 sessions).
Agentic RAG in practice: Typical application scenarios
E-Commerce
A visitor asks: "Which camera is suitable for wildlife photography in low light under €900?" Traditional RAG would likely start a text search in the product catalog and return the first match. The branchlyAI Engine goes further: It queries the product catalog, technical specifications, and FAQ content separately, evaluates the interim results, and combines the most relevant information into a genuine purchase recommendation — with justification and link. The result is not just an answer, but a consulting service.
Tourism
A visitor to a destination website asks: "What can I do with children aged 6 to 10 if it rains, and where can I eat well afterwards?" The question touches on activities, target group, weather conditions, and gastronomy — four areas that typically lie on different pages. branchly autonomously searches all relevant content areas and provides a consolidated recommendation — in the visitor's language, without the website itself needing to be multilingual.
Financial Services
A visitor asks about the difference between two savings models and wants to know which is more tax advantageous for a time horizon of five years. The answer requires product knowledge, regulatory information, and general tax notes — three sources that a traditional RAG system would not coordinate queries for. Agentic RAG breaks down the question, sequentially queries the relevant sources, and delivers a complete, GDPR-compliant answer. Sensitive follow-up questions can be automatically referred to a human advisor. branchly operates on Microsoft Azure in European data centers — GDPR and EU AI Act compliant.
Related terms
RAG (Retrieval-Augmented Generation)
Natural Language Processing (NLP)
AI Chatbot
AI Search
Hybrid Search
Agentic Commerce
Frequently Asked Questions
What is Agentic RAG in simple words?
Agentic RAG is an AI technology where an autonomous agent decides how to respond to a question — which sources to query, in what order, and whether it needs additional information after an initial retrieval. Instead of following a fixed process, the agent plans its research like an experienced employee and combines the results into a complete answer.
What is the difference between RAG and Agentic RAG?
Classic RAG performs a single retrieval: input question, output relevant documents, generate answer. Agentic RAG is multi-step and self-directed: the agent evaluates intermediate results, dynamically chooses between multiple sources, and initiates additional search iterations if necessary before generating the answer. This makes the crucial difference for complex, multi-part, or context-dependent questions.
Why does Agentic RAG reduce hallucinations better than classic RAG?
Because multiple, cross-source verification takes place. A simple RAG run might hit a single, possibly incomplete document and derive an incomplete answer from it. Agentic RAG checks multiple sources, evaluates the consistency of the information found, and only then generates an answer. According to Vectara (HHEM-Benchmark), RAG architecture already reduces hallucinations by 40 to 71% — Agentic RAG further improves this figure through iterative verification steps.
For what sizes of companies is Agentic RAG suitable?
Agentic RAG is not just for corporations. Platforms like branchly make the technology accessible for medium-sized enterprises in Europe — without their own AI infrastructure or developer teams. The starter plan starts at €499/month and covers 1,000 sessions. For companies with higher volumes, Pro and Enterprise plans are available.
How does Agentic RAG differ from a AI agent?
An AI agent is a general concept: an AI that makes decisions independently and performs actions. Agentic RAG is more specific: it describes the agent-based approach in the field of knowledge retrieval and answer generation. The agent in Agentic RAG specializes in combining information from structured and unstructured sources — not on general task execution. So the term is narrower than "AI agent" but broader than "classic RAG".
Is Agentic RAG implementable in compliance with GDPR?
Yes — if the implementation is based on EU-hosted infrastructure. branchly operates the branchlyAI Engine on Microsoft Azure in European data centers. All retrieval processes, user data, and conversation logs remain within the EU. This makes branchly GDPR-compliant, EU-AI-Act-ready, and usable for European companies without legal concerns.
How long does it take to implement Agentic RAG on an existing website?
With branchly, the basic setup takes just a few minutes. The branchlyAI Engine connects with your existing website content, product catalogs, and data sources — without requiring you to restructure or prepare content beforehand. For more complex setups with multiple system integrations (CRM, PIM, external databases), you should plan for one to two weeks.
What knowledge sources can Agentic RAG query?
That depends on the platform. The branchlyAI Engine autonomously queries website content, product catalogs, FAQ databases, and connected external sources. Which sources are relevant in what situation is determined by the engine independently — based on the inquiry. For companies, this means: you do not need to define a separate data source for every possible question.
Can Agentic RAG also process multilingual inquiries?
Yes. At branchly, the branchlyAI Engine natively understands inquiries in 101 languages and responds in the visitor's language — even if the source materials are only available in German or English. This is particularly valuable for companies with an international audience that do not want to build a separate multilingual content infrastructure.
How do I measure the success of Agentic RAG on my website?
The relevant metrics are: interaction rate (how many visitors ask questions), answer quality (are follow-up questions asked or is the conversation closed), deflection rate (how many support inquiries the system resolves itself), and conversion rate (how many users perform a desired action after an AI interaction). branchly provides these metrics as part of integrated visitor analytics. With over 40 million processed sessions, branchly has sufficient comparative data to provide benchmarks for various industries and company sizes (source: branchly, 2026).
