# Agentic RAG: Smarter Information Retrieval with Iterative Search

How do AI agents adopt an iterative approach to access information?

- **Source:** [Towards Data Science](https://towardsdatascience.com)
- **Published:** 2026-07-13
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://towardsdatascience.com/agentic-rag-let-the-agent-search/>
- **On Brifing:** https://brifing.si/s/67f9e1af-22ce-44a7-94ce-6cb8ee57aad9

## Challenges of RAG Applications

RAG applications can struggle to find relevant information chunks. Similar words may be found, but the right evidence often ranks low or is split across chunks. This leaves the AI model with insufficient context.

## The Idea of Iterative Search

Agentic RAG enables the model to search iteratively. It searches, reads, decides if it has enough evidence, and searches again if needed. This approach can reduce dependency on vector embeddings.

## Case Study: Company Policies

An Agentic RAG agent was built over a company policy collection. Six different policy documents were used to test the model's iterative search capabilities. These documents covered travel, remote work, and approval processes.

## Configuring the Agent

An agent was created using the OpenAI Agents SDK. The agent was equipped with three tools: listing documents, keyword searching, and reading a specific document. These tools enabled the agent to manage the iterative search process.

## How the Agent Works

The agent answered a policy question by first scanning documents, then finding relevant evidence, and finally using supporting documents. This process showed how the agent thinks and makes decisions.

## Example Question and Answer

The agent was tested on a hotel booking question for a conference. It provided the correct answer, identifying that the official conference hotel exceeded the normal cap and explaining the required approval conditions.

## Agent's Tracing Process

The agent's tracing process revealed how it thinks and follows steps. This tracing is an important tool for understanding the agent's iterative search process.

## The Future of Agentic RAG

Agentic RAG can significantly improve information retrieval. The iterative search approach enables AI models to provide more accurate and reliable answers. This technology could find broader applications in the future.

## Sources covering this story

- https://towardsdatascience.com/agentic-rag-let-the-agent-search/ — 

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_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._
