What is Agentic RAG? 

Retrieval-Augmented Generation (RAG) helps AI systems generate more relevant responses by retrieving information from enterprise knowledge sources.

Agentic RAG takes this approach even further. Instead of relying on a fixed information retrieval path, AI agents can decide what information they need, where to search, and whether additional information retrieval is necessary before generating a response.

This makes information retrieval more adaptable for complex enterprise questions involving multiple documents, tools, or repositories.

How Agentic RAG Goes Beyond Traditional Retrieval

Traditional RAG typically retrieves relevant information and passes it to an AI model to generate a response.

Agentic RAG adds reasoning and decision-making to this process. An agent can:

• Analyze the user’s request
• Select relevant data sources or tools
• Break down complex questions into smaller tasks
• Perform multiple searches if necessary
• Combine information from multiple sources
• Evaluate whether the information is enough
• Adjust its next action based on previous results

The key difference is that information search becomes an iterative process rather than a single, predefined step.

How Does Agentic RAG Work?

A typical Agentic RAG workflow might include:

1. Understanding the Request

The agent analyzes the question, objective, and context.

2. Creating a Retrieval Plan

The request can be divided into smaller queries or tasks as needed.

3. Choose sources or Tools

The agent identifies repositories, APIs, search systems, or tools that may contain the necessary information.

4. Retrieving and Evaluating Information

Relevant content is retrieved and assessed. If important information is missing, the agent can refine its query or use another source.

5. Generating a Based Response

The AI ​​generates a response using the information gathered during the retrieval process.

In simply terms:

1. Plan
2. Retrieve
3. Evaluate
4. Generate

The cycle can be repeated if additional information is needed.

Agentic RAG vs. Traditional RAG

Traditional RAG is generally suitable for predictable search workflows where relevant information can be found through a relatively direct search.

Agentic RAG is designed for more complex tasks and may require:

• Multiple retrieval steps
• Query decomposition
• Tool usage
• Different enterprise data sources
• Multiple document reasoning
• Decisions based on previous retrieval results

Traditional RAG focuses on finding information related to a query. Agentic RAG can determine what needs to be found in the next step to complete a task.

Why Agentic RAG Matters for Enterprises

Enterprise questions often require information from multiple documents or repositories.

For example:

“Does this supplier contract meet our current supply requirements?”

An AI agent might need to:

• Find the supplier contract
• Identify relevant clauses
• Retrieve the latest supply policy
• Find additional guidance if necessary
• Compare documents
• Generate a response based on the gathered information

This approach can support use cases such as:

• Contract and document analysis
• Technical troubleshooting
• Policy and regulation research
• Enterprise knowledge discovery
• Internal research assistants
• Multiple document comparison
• Complex enterprise search

What are the Security Risks of Agentic RAG?

More autonomous access also expands the security and governance surface.

An agent can interact with multiple repositories and tools before generating a response. Without proper controls, sensitive data exposure, this can increase the risk of over access, or unauthorized use of enterprise content.

Key control areas include:

• Permission-aware access
• Content-aware controls
• Least privilege access
• Sensitive data protection
• Source and tool governance
• Data residency
• Audit logging

The ability to discover information should never automatically grant permission for use.

Why Permission-Aware Retrieval is Critical?

Agentic RAG can make various access decisions from different enterprise sources. Therefore, authorization needs to remain effective throughout the workflow.

At each access step, the following should be considered:

• Who is making the request?
• Which files can this user access?
• Does the content contain sensitive information?
• Which repositories can the agent search?
• Is the selected AI model allowed to process content?
• Can the information appear in the final response?

Enterprise AI should never allow autonomous access to become truly autonomous.

How FileOrbis Helps

FileOrbis brings Agentic RAG to existing enterprise file environments without requiring data migration or a separate static knowledge base.

Instead of limiting the AI ​​to a predefined set of contents, four AI agents work on the ever-evolving enterprise knowledge, analyzing, finding, and presenting the right information while respecting each user’s existing access permissions.

With FileOrbis, organizations can:

• Keep enterprise files where they already live
• Use four AI agents to find, analyze, and retrieve relevant information
• Work with continuously evolving enterprise information
• Enforce permission-aware and content-aware controls
• Discover and classify sensitive data
• Maintain existing permissions throughout AI access
• Control how sensitive content is retrieved and used by AI
• Maintain audit logs across AI interactions

The result is Controlled and Practical Agentic AI that works with enterprise data where it already lives, while maintaining existing permissions and management controls.

In Summary

Agentic RAG makes access to information more adaptable by allowing AI agents to plan, search, evaluate, and adjust their actions based on the information they find.

For organizations, this can improve how AI handles complex questions in distributed information resources. However, greater access autonomy also requires stronger governance.

The key question is not just:

“Can AI find the right information?”

It is also:

“Is AI allowed to retrieve and use this information?”

For enterprise enterprises, Agentic RAG should make data retrieval more intelligent without bypassing existing access controls.

Frequently Asked Questions

What is Agentic RAG?

Agentic RAG is a Retrieval-Augmented Generation approach where AI agents can schedule retrieves, select tools or sources, evaluate results, and perform additional searches as needed.

How does Agentic RAG different from traditional RAG?

Traditional RAG typically follows a predefined search workflow. Agentic RAG can dynamically determine how to retrieve information and adapt its actions based on the results it receives.

Why is security important for Agentic RAG?

Agentic RAG can access multiple enterprise data sources and tools. Permission-aware searching, content-aware controls, least-privileged access, and audit logging help prevent unauthorized or sensitive information exposure.

Gamze Karslı
Head of Marketing