AI Agents vs RAG: How AI Uses Internal Documents and How to Combine

The content of this article was checked against each vendor's official documentation and public materials on October 2, 2026. Product features may change. Check the sources at the end for the latest details.

The difference between an AI agent and RAG lies in how the work ends. RAG ends when it returns an answer. An AI agent uses the answer to move on to the next action.

What is RAG: find documents first, then answer

RAG stands for Retrieval-Augmented Generation. Instead of relying only on what the LLM learned in training, it searches your own documents and builds an answer from what it finds. The official Dify documentation describes RAG in the three stages below.

The AI Guidelines for Business (version 1.2), issued by Japan's Ministry of Internal Affairs and Communications (MIC) and Ministry of Economy, Trade and Industry (METI), say that using RAG and similar methods is expected to reduce plausible-sounding errors (hallucinations) and make the basis of answers more transparent. The guidelines also say RAG is likely to make answers more alike. For that reason, RAG may not suit work that needs variety or originality.

In this article, the collection of documents that RAG searches is called knowledge. Dify also uses the word knowledge for company data gathered and built into an AI app, and it runs on the RAG mechanism.

The official Dify documentation lists uses for knowledge such as support chatbots that answer from product materials and FAQs, and internal search and Q&A over company rules and procedures. In each case, the documents behind the answer are already at hand.

What is an AI agent: research, act and finish the work

The AI Guidelines for Business (version 1.2) define an AI agent as an AI system that senses its environment and acts autonomously to achieve a specific goal. Anthropic describes the foundation of an agent as an LLM augmented with retrieval, tools and memory.

According to Anthropic, current models can use these abilities on their own: they write their own search queries, pick the right tools and decide what to remember. In RAG, search follows a flow that a person designed. In an AI agent, the AI can also decide what to search for.

In other words, search is one of the parts an AI agent has. After reading the search results, the agent moves on to writing to a spreadsheet, drafting an email or calling another tool. It repeats this until it judges the work is done. The parts and the loop are explained in How AI agents work.

The differences side by side

Which to use depends on whether you want an answer or want the work after the answer handled too. Anthropic writes that in many cases a single AI call improved with retrieval and examples is enough. If the job only needs an answer, not building an agent keeps things simpler.

Some work needs more than one search. Anthropic gives complex research that repeats search and analysis many times as an example. In this setup, a separate LLM evaluates the results and decides whether more searching is needed. Anthropic also cites search that gathers and analyzes related information from multiple sources as an example of splitting work and handing it off. For work like this, an AI agent that calls RAG many times is a good fit.

Using RAG as a tool for an AI agent

An AI agent can hold RAG as one of its tools. In a Dify Agent, when you add a knowledge base to the app, the model reads the knowledge base description for each question and decides whether to search. The official documentation says the more detailed the description, the more accurate this decision.

n8n also has a part that finds documents and answers. The Question and Answer Chain connects to a vector store so you can build a workflow that answers questions about specific documents. However, n8n chains cannot use memory, so the official documentation says to use an agent when you want a conversation that remembers earlier questions.

When you combine them, the cautions add up too. OWASP materials treat RAG as one of the core mechanisms of agents and list attacks that slip malicious content into documents, and indirect instruction hijacking through documents. As countermeasures, they point to limiting the search scope to the user's permissions, validating the data you import and continuously monitoring for contamination.

In an AI agent, the content of the documents it finds leads to its next action. To keep it from reading an instruction hidden in a document and then sending or writing something unintended, design it so a person approves any action with an effect outside the system.

What to decide when you use internal documents

• Documents to include: which documents go in, and how old versions are removed
• What may be shown: which documents an AI used by whom may search
• Personal information: whether documents or questions contain personal information
• Who updates: who fixes the knowledge when documents change
• Showing the basis: whether answers show which documents were used
• How to check: whether you test common questions to see if the intended documents are found

Dify has a screen for reviewing imported documents and chunks to add, edit or delete them, and a feature for entering a question to test search results. Building document updates and search tests into your operating procedures reduces answers drawn from outdated documents.

Japan's Personal Information Protection Commission (PPC) warns that when you enter prompts containing personal information into a generative AI service, you should fully confirm this is within the stated purpose of use. It says that entering personal data without the person's consent may violate the law if that data is handled for purposes other than producing the response. For that reason, it asks users to confirm, among other things, that the provider does not use the data for machine learning.

A RAG example with Dify knowledge

There are three ways to create a knowledge base in Dify: import documents and pick settings, build the import pipeline yourself, or connect to an external knowledge base through an API. A company that already has a document search system elsewhere can connect to it without moving anything.

In Dify, you build RAG by creating a knowledge base, importing documents and connecting it to an app. Imported documents are split into small units called chunks and stored. The chunking mode cannot be changed after the knowledge base is created. The delimiter and chunk length can be changed later.

Chunks are described as being like splitting a large book into chapters and paragraphs. When a question comes in, the system finds the related parts among the chunks and passes them to the LLM. One mode splits everything with the same settings. Another searches small chunks and returns the larger chunk that contains them.

In the search settings, you choose between setting weights for semantic similarity and keyword matching, or using a rerank model. The weight option uses no external model, so there is no added cost. A rerank model needs an API key from an external provider and adds cost. The default number of results returned (TopK) is 3. You can also set a minimum similarity score to drop weakly related results.

You can also add a citation display that shows which documents an answer used. If you attach metadata to documents, you can narrow the search to specific documents.

Detailed Dify setup steps are covered in How to use Dify knowledge, and use in customer inquiries in AI agents for the help desk.

Choosing an AI employee that uses internal documents on Employee Store

Employee Store is a marketplace where companies can adopt AI agents (AI employees) built by developers. Pricing is one-time, monthly, or an upfront fee plus monthly, and payments are processed by Stripe.

Categories include customer support, back office and sales support. When choosing an AI employee that uses internal documents, check on the listing page where documents are imported, and whether it only returns answers or goes on to record or send. After purchase, you can message the seller on the page for each deal and agree on the six points above.