What is replacing RAG in agentic AI?

Contextual memory and enhanced RAG approaches like GraphRAG are emerging as successors to traditional RAG for more adaptive AI.

Are purpose-built vector databases still needed in 2026?

Yes, but their use cases are narrowing as general-purpose databases increasingly support vector data, though high-performance needs remain.

Why is PostgreSQL becoming popular for GenAI?

PostgreSQL's open-source foundation, flexibility, and strong performance make it the preferred choice for building GenAI solutions.

Home / Technology / AI Data: RAG is Dead, Long Live Memory

AI Data: RAG is Dead, Long Live Memory

1 Jan

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Summary

RAG architecture faces limitations, evolving into enhanced approaches.
Contextual memory is becoming essential for agentic AI workflows.
PostgreSQL is the ascendant database for building GenAI solutions.

As agentic AI advances, the data infrastructure is undergoing rapid transformation. Traditional RAG pipelines, limited by static retrieval, are giving way to enhanced approaches and contextual memory, essential for AI that learns and adapts. While RAG won't disappear, contextual memory is becoming a foundational requirement for sophisticated AI workflows.

Purpose-built vector databases are also evolving. The integration of vector data types into multimodal databases and even object storage is reducing the need for specialized systems. However, niche use cases requiring peak performance will still favor dedicated solutions.

Meanwhile, PostgreSQL is emerging as the dominant database for GenAI. Significant investments in PostgreSQL vendors underscore its growing relevance due to its flexibility, open-source nature, and performance. Acquisitions and consolidation among data vendors are also expected to continue, shaping the future of AI-sustained data infrastructure.