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


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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:

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 ...


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