Oracle Announces Agentic AI Innovations for Business Data AI Database
Oracle has unveiled new agentic AI innovations for its Oracle AI Database, enabling customers to build, deploy, and scale secure agentic AI applications for production workloads. This database integrates agentic AI and data from operational databases to analytical lakehouses, allowing AI agents to securely access real-time enterprise data and combine it with LLMs for business insights. Key features include the Oracle Autonomous AI Vector Database, Oracle AI Database Private Agent Factory for no-code agent building, and Oracle Unified Memory Core for consistent context across multiple AI agents.
📋 Article Processing Timeline
- 📰 Published: April 3, 2026 at 21:00
- 🔍 Collected: April 3, 2026 at 12:30
- 🤖 AI Analyzed: April 18, 2026 at 02:25 (349h 54m after Collected)
(This document is a translation of a press release announced by Oracle Corporation in the United States on March 24, 2026)
**Oracle AI World Tour, London — April 3, 2026**
Today, Oracle announced new agentic AI innovations for "Oracle AI Database." This enables customers to quickly build, deploy, and scale secure agentic AI applications suitable for full-scale production workloads. "Oracle AI Database" is designed to integrate agentic AI and data across the entire spectrum, from operational databases to analytical lakehouses. This allows AI agents to securely access real-time enterprise data regardless of its location, and easily combine business data with LLMs trained on public data to provide business insights. Customers can choose from many AI models, agentic frameworks, open data formats, and deployment platforms. Furthermore, customers using "Oracle Exadata" can leverage "Exadata Powered AI Search" to deploy agentic AI at an unprecedented scale, achieving high-speed processing of AI queries in large-scale, multi-stage agentic workloads.
Juan Loaiza, Executive Vice President of Oracle Database Technologies at Oracle, stated: "The next wave of enterprise AI will hinge on the ability to securely leverage AI in mission-critical production systems to drive unprecedented innovation, insights, and productivity gains. 'Oracle AI Database' allows data to be utilized for AI, not just stored. By designing AI and data as one, we enable secure querying and processing of real-time enterprise data with stock exchange-level robustness across all leading cloud and on-premises environments, helping customers quickly build and manage agentic AI applications."
**Accelerating Innovation with AI Designed for Data**
With agentic AI features specifically designed for data utilization, "Oracle AI Database" eliminates the need to build and maintain complex, security-risky, and potentially detrimental data movement pipelines. New features include:
* **Oracle Autonomous AI Vector Database** provides a simple way to leverage the full power of "Oracle AI Database" as a vector database. It enables developers and data scientists to quickly and easily build vector applications using intuitive APIs and user-friendly web interfaces. Built on "Oracle Autonomous AI Database," it offers an easy-to-use developer experience and enterprise-class security, reliability, and scalability. "Autonomous AI Vector Database" is currently available in limited release and can be accessed through the "Oracle Cloud" Free Tier or a low-cost developer tier. As requirements expand, it can be upgraded to the full-power "Oracle Autonomous AI Database" with a single click, fully supporting a wide range of features including graph, spatial, JSON, relational, text, and parallel SQL. This eliminates the need for separate purpose-built databases and complex inter-database agentic workflows.
* **Oracle AI Database Private Agent Factory** enables business analysts and industry experts to quickly build and securely deploy data-driven agents and workflows. "AI Database Private Agent Factory" provides a no-code AI agent builder, allowing them to run as containers in the public cloud or on-premises. This allows AI agents to be built, deployed, and managed without sharing data with third parties, maintaining data security. "AI Database Private Agent Factory" includes several pre-built data-specific AI agents, such as database knowledge agents, structured data analysis agents, and deep data research agents. This eliminates the need to rely on external agent integrations or call multiple different types of databases. The design of "Oracle AI Database" with integrated agentic AI makes agentic AI simpler and more consistent for business users. It also provides enterprise-class security, resilience, and scalability for all agentic workloads.
* **Oracle Unified Memory Core** allows context used across multiple AI agents to be stored in a single system. Furthermore, it enables low-latency inference for vector, JSON, graph, relational, text, spatial, and columnar data on a single integrated engine, ensuring transactional consistency and security.
**Minimizing AI Data Risk**
"Oracle AI Database" helps customers protect their data from external attacks, internal fraud, accidental leaks, and unintended exposure to LLMs across multi-cloud, hybrid, and on-premises environments. New features include:
* **Oracle Deep Data Security** implements strong per-end-user data access rules within the database. Each end-user, or an AI agent acting on behalf of an end-user, can only see the data that end-user is authorized to view. Furthermore, advanced rules based on personas and job functions can be implemented. For example, it can define which parts of a customer account a specific sales representative, finance representative, shipping representative, executive, support representative, or even a relative of the customer themselves can view. This is a unique security feature to protect data from new threats in the AI era, such as prompt injection. It uses declarative controls built into the database to securely access data with "least privilege." By centralizing security separate from application code, it becomes easy to manage who can view what data. It also allows access rules to be flexibly updated as new threats emerge, providing effective guardrails for agents operating on "Oracle AI Database." Securing data at its source in the database provides better protection when AI agents directly access data on behalf of end-users.
* **Oracle Private AI Services Container** allows customers with strict security requirements to run private instances of AI models without sharing data with third-party AI providers or sending it outside the firewall. It also helps mitigate performance bottlenecks by securely offloading computationally intensive AI tasks, such as generating vector embeddings, outside the database. This helps keep all data secure within the customer's environment. This container can be deployed in public cloud, private cloud, on-premises, and even air-gapped environments.
* **Oracle Trusted Answer Search** provides enterprises with an accurate, verifiable, and reproducible approach to providing answers to end-users using AI. Instead of directly answering end-user questions with an LLM, it uses "AI Vector Search" to match questions to pre-created reports. This helps mitigate the risk of probabilistic LLMs occasionally hallucinating or misunderstanding queries.
**Eliminating AI Data Lock-in with Open Standards and Frameworks**
"Oracle AI Database" runs on major cloud providers and supports hybrid and on-premises deployments, giving customers the flexibility to choose the AI models and application-layer agentic frameworks that best suit their needs. It also allows them to build, deploy, and run agentic AI applications using open standards and data formats. New features include:
* **Oracle Vectors on Ice** natively supports vector data stored in Apache Iceberg tables. AI vector search can directly read vector data from Iceberg tables. Vector indexes can also be created to accelerate vector search, and the indexes are automatically updated when the original vector data changes. "Oracle Vectors on Ice" enables AI search on data within data lakes, allowing for integrated search across both business data in the database and vectors stored in the data lake. This provides customers with integrated insights across databases and data lakes.
* **Oracle Autonomous AI Database MCP Server** allows external AI agents and MCP clients to securely access "Oracle Autonomous AI Database" and its features without custom integration code or manual security management. It complements the "Oracle SQLcl MCP Server" for "Oracle AI Database," which is available through the VS Code extension for "Oracle SQL Developer."
Steven Dickens, CEO and Principal Analyst at HyperFRAME Research, stated: "In the era of agentic AI, an integrated memory core is essential for agents to maintain consistent context across diverse data types—including vector, JSON, graph, columnar, spatial, text, and relational—without the latency of external synchronization or data staleness. Only 'Oracle AI Database' can perform real-time inference on business data while simultaneously handling transactional and analytical processing in a mission-critical engine, ensuring high availability and robust security. Organizations without this foundation will suffer from fragmented and unreliable agents. In contrast, organizations leveraging Oracle will gain an overwhelming advantage in scalable AI adoption."
Customers and developers can immediately begin developing and deploying innovative agentic AI applications using the new agentic AI features for "Oracle AI Database," without needing to move data, learn new skills, or worry about database scalability or insufficient agentic AI security. For the latest AI innovations, please refer to the [Oracle AI Database Agentic AI Announcement Blog](https://blogs.oracle.com/database/oracle-ai-database-delivers-mission-critical-agentic-ai-built-for-business-data).
**About Oracle**
Oracle offers an integrated suite of applications and secure, autonomous infrastructure in the Oracle Cloud. For more information about Oracle (NYSE: ORCL), please visit www.oracle.com.
**Trademarks**
Oracle, Java, MySQL and NetSuite are registered trademarks of Oracle Corporation, its subsidiaries and affiliates in the U.S. and other countries. NetSuite is the cloud company that pioneered the new era of cloud computing.
**Oracle AI World Tour, London — April 3, 2026**
Today, Oracle announced new agentic AI innovations for "Oracle AI Database." This enables customers to quickly build, deploy, and scale secure agentic AI applications suitable for full-scale production workloads. "Oracle AI Database" is designed to integrate agentic AI and data across the entire spectrum, from operational databases to analytical lakehouses. This allows AI agents to securely access real-time enterprise data regardless of its location, and easily combine business data with LLMs trained on public data to provide business insights. Customers can choose from many AI models, agentic frameworks, open data formats, and deployment platforms. Furthermore, customers using "Oracle Exadata" can leverage "Exadata Powered AI Search" to deploy agentic AI at an unprecedented scale, achieving high-speed processing of AI queries in large-scale, multi-stage agentic workloads.
Juan Loaiza, Executive Vice President of Oracle Database Technologies at Oracle, stated: "The next wave of enterprise AI will hinge on the ability to securely leverage AI in mission-critical production systems to drive unprecedented innovation, insights, and productivity gains. 'Oracle AI Database' allows data to be utilized for AI, not just stored. By designing AI and data as one, we enable secure querying and processing of real-time enterprise data with stock exchange-level robustness across all leading cloud and on-premises environments, helping customers quickly build and manage agentic AI applications."
**Accelerating Innovation with AI Designed for Data**
With agentic AI features specifically designed for data utilization, "Oracle AI Database" eliminates the need to build and maintain complex, security-risky, and potentially detrimental data movement pipelines. New features include:
* **Oracle Autonomous AI Vector Database** provides a simple way to leverage the full power of "Oracle AI Database" as a vector database. It enables developers and data scientists to quickly and easily build vector applications using intuitive APIs and user-friendly web interfaces. Built on "Oracle Autonomous AI Database," it offers an easy-to-use developer experience and enterprise-class security, reliability, and scalability. "Autonomous AI Vector Database" is currently available in limited release and can be accessed through the "Oracle Cloud" Free Tier or a low-cost developer tier. As requirements expand, it can be upgraded to the full-power "Oracle Autonomous AI Database" with a single click, fully supporting a wide range of features including graph, spatial, JSON, relational, text, and parallel SQL. This eliminates the need for separate purpose-built databases and complex inter-database agentic workflows.
* **Oracle AI Database Private Agent Factory** enables business analysts and industry experts to quickly build and securely deploy data-driven agents and workflows. "AI Database Private Agent Factory" provides a no-code AI agent builder, allowing them to run as containers in the public cloud or on-premises. This allows AI agents to be built, deployed, and managed without sharing data with third parties, maintaining data security. "AI Database Private Agent Factory" includes several pre-built data-specific AI agents, such as database knowledge agents, structured data analysis agents, and deep data research agents. This eliminates the need to rely on external agent integrations or call multiple different types of databases. The design of "Oracle AI Database" with integrated agentic AI makes agentic AI simpler and more consistent for business users. It also provides enterprise-class security, resilience, and scalability for all agentic workloads.
* **Oracle Unified Memory Core** allows context used across multiple AI agents to be stored in a single system. Furthermore, it enables low-latency inference for vector, JSON, graph, relational, text, spatial, and columnar data on a single integrated engine, ensuring transactional consistency and security.
**Minimizing AI Data Risk**
"Oracle AI Database" helps customers protect their data from external attacks, internal fraud, accidental leaks, and unintended exposure to LLMs across multi-cloud, hybrid, and on-premises environments. New features include:
* **Oracle Deep Data Security** implements strong per-end-user data access rules within the database. Each end-user, or an AI agent acting on behalf of an end-user, can only see the data that end-user is authorized to view. Furthermore, advanced rules based on personas and job functions can be implemented. For example, it can define which parts of a customer account a specific sales representative, finance representative, shipping representative, executive, support representative, or even a relative of the customer themselves can view. This is a unique security feature to protect data from new threats in the AI era, such as prompt injection. It uses declarative controls built into the database to securely access data with "least privilege." By centralizing security separate from application code, it becomes easy to manage who can view what data. It also allows access rules to be flexibly updated as new threats emerge, providing effective guardrails for agents operating on "Oracle AI Database." Securing data at its source in the database provides better protection when AI agents directly access data on behalf of end-users.
* **Oracle Private AI Services Container** allows customers with strict security requirements to run private instances of AI models without sharing data with third-party AI providers or sending it outside the firewall. It also helps mitigate performance bottlenecks by securely offloading computationally intensive AI tasks, such as generating vector embeddings, outside the database. This helps keep all data secure within the customer's environment. This container can be deployed in public cloud, private cloud, on-premises, and even air-gapped environments.
* **Oracle Trusted Answer Search** provides enterprises with an accurate, verifiable, and reproducible approach to providing answers to end-users using AI. Instead of directly answering end-user questions with an LLM, it uses "AI Vector Search" to match questions to pre-created reports. This helps mitigate the risk of probabilistic LLMs occasionally hallucinating or misunderstanding queries.
**Eliminating AI Data Lock-in with Open Standards and Frameworks**
"Oracle AI Database" runs on major cloud providers and supports hybrid and on-premises deployments, giving customers the flexibility to choose the AI models and application-layer agentic frameworks that best suit their needs. It also allows them to build, deploy, and run agentic AI applications using open standards and data formats. New features include:
* **Oracle Vectors on Ice** natively supports vector data stored in Apache Iceberg tables. AI vector search can directly read vector data from Iceberg tables. Vector indexes can also be created to accelerate vector search, and the indexes are automatically updated when the original vector data changes. "Oracle Vectors on Ice" enables AI search on data within data lakes, allowing for integrated search across both business data in the database and vectors stored in the data lake. This provides customers with integrated insights across databases and data lakes.
* **Oracle Autonomous AI Database MCP Server** allows external AI agents and MCP clients to securely access "Oracle Autonomous AI Database" and its features without custom integration code or manual security management. It complements the "Oracle SQLcl MCP Server" for "Oracle AI Database," which is available through the VS Code extension for "Oracle SQL Developer."
Steven Dickens, CEO and Principal Analyst at HyperFRAME Research, stated: "In the era of agentic AI, an integrated memory core is essential for agents to maintain consistent context across diverse data types—including vector, JSON, graph, columnar, spatial, text, and relational—without the latency of external synchronization or data staleness. Only 'Oracle AI Database' can perform real-time inference on business data while simultaneously handling transactional and analytical processing in a mission-critical engine, ensuring high availability and robust security. Organizations without this foundation will suffer from fragmented and unreliable agents. In contrast, organizations leveraging Oracle will gain an overwhelming advantage in scalable AI adoption."
Customers and developers can immediately begin developing and deploying innovative agentic AI applications using the new agentic AI features for "Oracle AI Database," without needing to move data, learn new skills, or worry about database scalability or insufficient agentic AI security. For the latest AI innovations, please refer to the [Oracle AI Database Agentic AI Announcement Blog](https://blogs.oracle.com/database/oracle-ai-database-delivers-mission-critical-agentic-ai-built-for-business-data).
**About Oracle**
Oracle offers an integrated suite of applications and secure, autonomous infrastructure in the Oracle Cloud. For more information about Oracle (NYSE: ORCL), please visit www.oracle.com.
**Trademarks**
Oracle, Java, MySQL and NetSuite are registered trademarks of Oracle Corporation, its subsidiaries and affiliates in the U.S. and other countries. NetSuite is the cloud company that pioneered the new era of cloud computing.