​​RAG is dead: why enterprises are shifting to agent-based AI architectures

Recently, a new sentiment has emerged in AI security circles: "RAG is dead." I've observed firsthand how organizations are increasingly abandoning Retrieval-Augmented Generation (RAG) architectures in favor of agent-based approaches.

This shift represents not a temporary trend but a fundamental recognition of RAG's inherent security and performance limitations.

The inherent flaws in RAG architectures

When enterprises first implemented AI applications, RAG emerged as the default architecture -- extracting data from internal systems, centralizing it in vector databases and using it to augment AI model outputs. While conceptually straightforward, this approach revealed critical vulnerabilities at scale:

• The security debt of browsing AI agents
• Agentic AI and ransomware: get ready for the next threat evolution
• How Agentic AI transforms enterprise automation

RAG architectures create significant security risks by centralizing data from disparate systems into repositories that frequently bypass the original access controls. These centralized stores become potential ...