RAG vs Agentic RAG Explained: How They Work and Which to Choose
Maxime Vermeir
January 15, 2026
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Frequently asked questions
Can I start with RAG and upgrade to agentic RAG later?
Yes. Many organizations begin with RAG to ground language models in verified data. Once that’s established, you can extend into agentic RAG and scale toward more autonomous, goal-driven systems as data maturity grows.
What is the difference between RAG and agentic RAG?
RAG enhances LLMs by retrieving and grounding their responses in external knowledge sources. Agentic RAG, builds on this by enabling the system to reason, decide, and act based on the information it retrieves.
What industries benefit most from agentic RAG?
Highly regulated industries such as finance, insurance, and healthcare gain clear value, but any organization with documents can benefit, because when document data becomes structured and accessible, it becomes actionable.
Can ABBYY integrate with RAG and LLM platforms?
Yes. ABBYY Document AI works seamlessly with RAG platforms and LLMs through APIs and ready-to-use connectors. It sends structured, validated data straight into vector databases and retrieval pipelines to improve how models reason and respond.
Maxime Vermeir
VP of AI Strategy at ABBYY
Maxime Vermeir is Vice President of AI Strategy at intelligent automation company ABBYY. He focuses on helping enterprises move artificial intelligence from experimentation to execution, translating advances in large language models (LLMs) and other AI technologies into systems that deliver measurable business outcomes.
Working at the intersection of strategy, process, and technology, Maxime advises organizations on how to operationalize AI at scale across real-world workflows. He also hosts ABBYY’s AI Pulse podcast, where he explores emerging AI trends and their practical impact on enterprises.
Generative AI can perform impressive tasks, but it has a well-known flaw: It sometimes “hallucinates” and makes up answers that sound right but aren’t.
RAG helps LLMs give more accurate, trustworthy answers by linking them to external data sources with relevant information. Agentic RAG takes this technology further to also plan, decide, and act on those answers. To put it simply, RAG retrieves facts, while agentic RAG uses those facts to make decisions and take actions.
Let’s take a look at how both traditional RAG and agentic RAG work, what their benefits and limitations are, and how to choose the right one for your business.
RAG is an AI approach designed to make LLMs more accurate and reliable. Instead of relying only on the data they were trained on, RAG systems connect LLMs to trusted documents or databases to pull in up-to-date, relevant materials before generating a response. This way, LLMs can ground their answers in real evidence and give more reliable, context-aware answers.
The main benefits of RAG include:
Enhanced contextual understanding: By reasoning across multiple data sources, agentic RAG connects related information for more comprehensive insights.
Improved accuracy: Grounded in structured, validated data, RAG produces responses that are more precise and aligned with enterprise knowledge.
Reduced hallucinations: Every output is based on retrieved, verifiable information, so the risk of fabricated or incorrect content is significantly lower.
Key components of RAG
RAG systems rely on a number of components that work together:
Embedding model: An embedding model converts words, sentences, or documents into sets of numbers called vectors. These vectors capture context and relationships between ideas, so systems can recognize meaning and relevance, even when different words are used.
Vector store: A vector store houses the vectors and enables semantic search, retrieving information based on meaning and context. This lets RAG systems find the most relevant content from a pre-indexed knowledge base.
Retriever: When a user submits a question, a retriever searches the vector database to identify and rank the most contextually relevant documents.
Prompt construction and generator: The relevant information gets combined with the user’s original question and is sent to the LLM. Using both its general training and the retrieved facts, the LLM then generates a final response.
Challenges and limitations of RAG
Traditional RAG, however, comes with some limitations:
Static retrieval: RAG relies on a fixed, pre-indexed knowledge base. It can’t dynamically update in real time, so responses can get outdated.
Limited contextual understanding: RAG must receive accurate and organized data to function well. If the information it's given is disconnected or incomplete, RAG’s outputs will not be as accurate.
No multi-step reasoning: Traditional RAG retrieves and summarizes information, but can’t reason or plan across several steps to make decisions or take actions based on what it finds.
Understanding how traditional RAG works
Here’s how the traditional RAG workflow typically works:
User prompt: A user asks the RAG system a question or gives it a prompt.
Retrieval: The system combs through its knowledge base to pinpoint and draw from the most relevant, trusted information.
Integration: The retrieved information is combined with the user’s original prompt to create an augmented prompt: an input that includes accurate and contextual background.
LLM output: The LLM uses this prompt to generate a response that’s more accurate and traceable to its original sources.
What is agentic RAG?
Agentic RAG strengthens LLMs by not only grounding their responses in external, verifiable sources but also adding the ability to reason, make decisions, and take action based on what they learn.
Basically, while traditional RAG simply gives responses, agentic RAG can also act on these responses like an active participant in problem-solving.
Understanding how agentic RAG works
Agentic RAG adds reasoning, decision-making, and action to the traditional RAG workflow:
User prompt: As with traditional RAG, the agentic RAG process begins with a user question or prompt that defines the task for the system to solve.
Retrieval: The model searches its knowledge base using semantic retrieval to find the most relevant information, just as in traditional RAG.
Reasoning and planning: The agent interprets what it finds to decide if the retrieved data is sufficient or if more context is needed to deliver a complete and accurate answer.
Iterative retrieval: If more context or data is required, the agent runs more searches to refine or validate facts until it reaches a confident understanding.
Augmented prompt: The agent uses the information it's gathered to come up with a refined prompt for the LLM.
LLM output: The LLM generates a response or action plan that’s grounded in the retrieved information.
Validation: The agent checks the output against trusted sources or business rules and triggers next steps as needed.
RAG vs Agentic RAG: Key differences at a glance
Traditional RAG improves LLM accuracy, while agentic RAG goes further to identify gaps, retrieve additional data when needed, validate information, and take action.
RAG
Agentic RAG
Definition
AI architecture that improves LLM performance by connecting them to external knowledge sources
Advanced RAG architecture that incorporates intelligent agents to reason, plan, validate, and act
Use case
Knowledge retrieval, FAQs, summarizing information
Complex tasks like: decision support, automated workflows, multi-step problem solving
Adaptability
Relies on a pre-indexed knowledge base and reacts only to user prompts
Performs multiple searches to refine results as necessary
Accuracy
Grounds responses in external data to improve precision
Further improves precision by validating and cross-checking responses
Focus
Grounds responses in trustworthy information to reduce hallucinations
Uses grounded knowledge to reason, decide, and take action
Limitation
Dependent on the quality and scope of its data sources; limited to retrieval and summarization
Dependent on the quality and scope of its data sources
Efficiency and cost
Generally faster and less resource-intensive
More capable, but also more expensive and complex to design and operate
In short, RAG helps LLMs provide context-grounded answers quickly, while agentic RAG adds reasoning and validation for more accurate, reliable outcomes.
Why agentic RAG alone can’t work in isolation
As versatile and powerful as agentic RAG is, used on its own, the technology comes up against limits:
Dependence on data quality: Even the most capable agent can act only on the information it has. If the information given to an agentic RAG is inconsistent or unstructured, you can get unreliable outcomes.
Increased cost and latency: Repeated data retrieval helps make LLM outputs more accurate, but the extra work also requires more processing power and time. That, combined with the resources needed for agentic RAG’s multi-step reasoning, can increase costs.
Higher complexity: Automating end-to-end workflows requires coordinating agents and platforms with business systems. This can be a challenge if the technologies aren’t compatible or don’t work easily with each other.
How ABBYY builds a strong foundation for RAG and agentic RAG
Strong data quality is the foundation of any successful RAG system, traditional or agentic. This is what ABBYY Document AI provides. By turning unstructured content into clean, structured, and semantically rich data, ABBYY gives RAG models a trusted base of truth to reason from. Agentic RAG delivers intelligence, but ABBYY delivers the reliable data that makes that intelligence work.
By automating document extraction and structuring, ABBYY also reduces the noise and redundancy that slow down retrieval and reasoning. Additionally, ABBYY simplifies the technical side of deploying agentic RAG by offering structured data pipelines and API-based integrations.
