As enterprises pivot from experimental chatbots to autonomous, agentic AI systems that can reason, plan, and execute complex tasks, many are running into an architectural ceiling created by the last decade’s “polyglot persistence” approach. Stitching together specialized databases for different data types fragments the enterprise data estate and introduces latency, complexity, and governance risk—precisely where AI agents need a unified, real-time view of the truth.
Agentic AI raises the bar for the data layer. AI agents require multi-model fluency across documents, relational data, and vectors; zero-latency context so retrieval-augmented generation (RAG) stays grounded in the present; and transactional-grade integrity when agents are authorized to take action. When these capabilities are bolted together across multiple platforms, the “cognitive tax” shifts compute cycles from reasoning to integration.
In our latest report, The Agentic Frontier: Why Converged Data Engines are the Optimal Foundation for Autonomous Enterprise AI, commissioned by Oracle, Futurum Research analyzes the architectural divergence between the specialized document-store model exemplified by MongoDB Atlas and the converged, multi-model engine of Oracle Autonomous AI Database—and explains why enterprise AI builders are increasingly demanding a single foundation where JSON, relational, vectors, and more can co-exist without data movement or duplication.
In this report, you will learn:
- Why agentic AI imposes three non-negotiable data requirements: multi-model fluency, real-time context, and transactional agency
- How converged data architectures can reduce fragmentation, integration complexity, and governance gaps that stall AI initiatives
- How Oracle’s JSON Relational Duality Views enable simultaneous JSON and relational access to the same data—without duplication
- Why native, in-kernel vector processing can improve RAG freshness and reduce hallucinations versus “sidecar” indexing approaches
- How modernization can be achieved without a full rewrite using wire-protocol compatibility for existing MongoDB applications
If you are interested in learning more, be sure to download your copy of The Agentic Frontier: Why Converged Data Engines are the Optimal Foundation for Autonomous Enterprise AI today.
Author Information
Data Intelligence, Analytics, & Infrastructure Practice Led by Brad Shimmin
The Data Intelligence, Analytics, & Infrastructure Practice Area examines how enterprise data architectures are being redesigned for the AI era. As organizations shift from AI experimentation to production-scale engineering in 2026, data is no longer simply an operational asset—it is the foundational infrastructure that determines whether AI initiatives succeed or fail. This practice tracks how the modern data stack is evolving across four interconnected pillars: high-performance data foundations and storage layers purpose-built to feed GPU-accelerated and agentic systems; composable data processing and orchestration architectures that elevate the semantic layer into critical AI infrastructure; the reinvention of analytics into generative, AI-driven intelligence workflows; and proactive data management frameworks that embed governance, observability, and FinOps as active controls to ensure trust, performance, and cost discipline. Together, these shifts reflect a market moving beyond “AI-ready” toward data platforms explicitly engineered to accelerate, operationalize, and govern AI at scale.
