Agentic RAG: Let the Agent Search

2026-07-13 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, medium

Summary

Agentic RAG addresses the limitations of traditional RAG applications, where static similarity search often fails to retrieve useful context. This approach enables a large language model to iteratively search, read, and decide on evidence, searching again if needed, potentially reducing reliance on vector embeddings. A case study demonstrates building a policy RAG agent using the OpenAI Agents SDK and a "gpt-5.4" model. This agent, equipped with "list_docs", "search_docs", and "read_doc" tools, successfully answered complex policy questions by iteratively accessing six synthetic company policy documents, including "approval_matrix.md" and "travel_policy.md", and grounding its responses in the retrieved information.

Key takeaway

For AI Engineers evaluating RAG architectures, consider agentic RAG for complex questions requiring iterative information gathering. While it offers greater flexibility in evidence collection, be aware of increased latency, token costs, and less predictable behavior compared to simpler RAG setups. Start with curated tools and a single agent, only adopting multi-agent strategies or broader access when task complexity genuinely necessitates iterative retrieval and dynamic search capabilities.

Key insights

Agentic RAG empowers LLMs to iteratively search, read, and decide on evidence, overcoming static RAG limitations.

Principles

• Curated tools provide control and auditability.
• Knowledge layers enhance document navigation.
• Embeddings can still power agent search tools.

Method

Configure an agent with instructions and specific tools like "list_docs", "search_docs", and "read_doc" to enable iterative document investigation and grounded answer generation.

In practice

• Develop policy research agents.
• Integrate OpenAI Agents SDK.
• Split agent tasks by role.

Topics

• Agentic RAG
• LLM Agents
• Retrieval-Augmented Generation
• OpenAI Agents SDK
• Information Retrieval
• Multi-agent Systems

Code references

• ShuaiGuo16/agentic_RAG

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.