Oracle launches agentic AI tools for enterprise data management

2026-03-24 · Source: Tech Monitor · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Advanced, quick

Summary

Oracle has significantly expanded its AI Database platform with new agentic AI capabilities, enabling direct integration of AI agents with operational databases and analytic lakehouses. Key enhancements include Exadata Powered AI Search for accelerated multi-step agentic workloads, the Oracle Autonomous AI Vector Database for streamlined vector-driven application development, and the Oracle AI Database Private Agent Factory for no-code agent construction by domain experts. The platform also introduces a Unified Memory Core for diverse data types, Deep Data Security with granular access rules, and a Private AI Services Container for secure, on-premises AI model operation. Additionally, Oracle Trusted Answer Search aims to reduce LLM errors, and the platform now supports open standards like Apache Iceberg for vector data.

Key takeaway

For CTOs and VPs of Engineering evaluating AI infrastructure, Oracle's expanded AI Database offers a compelling solution for deploying agentic AI applications directly on enterprise data. You can leverage features like the Private Agent Factory and Deep Data Security to ensure data privacy and secure access, while open standards support mitigates vendor lock-in. Consider piloting the Autonomous AI Vector Database to accelerate vector-driven application development.

Key insights

Oracle's expanded AI Database integrates agentic AI directly with enterprise data, enhancing security, privacy, and performance.

Principles

• AI and data integration is crucial for robust agentic applications.
• Data privacy must be maintained within customer control.
• Open standards prevent vendor lock-in.

Method

The platform enables building and deploying data-driven agents using a no-code builder, vector search, and unified memory for diverse data types, ensuring secure access and privacy.

In practice

• Develop vector-driven apps via streamlined APIs.
• Construct data-driven agents using a no-code builder.
• Operate private AI models within organizational firewalls.

Topics

• Agentic AI
• AI Vector Database
• Enterprise Data Integration
• Data Security
• Large Language Models

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Monitor.