Oracle Unveils Agentic AI Innovations to Securely Activate Business Data Across Multicloud Environments

Bringing Intelligence to the Data Layer

Oracle has launched a suite of agentic AI capabilities natively integrated into its AI Database to help organizations deploy secure, production-grade autonomous agents. Announced at the Oracle AI World Tour in London on March 24, 2026, these innovations allow AI agents to securely access real-time enterprise data across operational databases and analytic lakehouses. This shift aims to eliminate the complex data pipelines usually required to move information between storage and AI models, reducing both latency and security risks. Central to the release is the AI Database Private Agent Factory, a no-code tool for building data-driven workflows without sharing information with third parties. Oracle also introduced the Unified Memory Core, which stores agent context across various data types like JSON, graph, and vectors in a single engine. This architecture ensures that agents maintain consistent context without the staleness often associated with syncing external memory stores. More details can be found via the official Oracle AI Database page. To combat modern threats like prompt injection, Oracle implemented Deep Data Security to enforce strict user-specific access rules at the database level. Other tools like Trusted Answer Search help prevent AI hallucinations by matching queries to validated reports rather than relying solely on probabilistic models. These guardrails ensure that AI agents only retrieve and process data that the requesting end-user is explicitly authorized to view, maintaining enterprise-grade safety.

Market Competition and Industry Standards

While competitors like Snowflake and Databricks offer robust data platforms, Oracle differentiates itself by embedding the agent orchestration and memory directly into the database engine. Most hyperscalers still require customers to manage fragmented architectures across separate vector stores and external orchestration layers. Oracle’s use of the Model Context Protocol (MCP) further allows external agents to connect to the database securely without custom integration code, supporting broader industry interoperability. Juan Loaiza, executive vice president at Oracle, noted that this architecture allows customers to activate their data for AI rather than just storing it. The goal is to provide stock exchange-level robustness for agentic workloads in every cloud environment, including on-premises and air-gapped systems. As the data and AI market is projected to exceed 1.2 trillion dollars by 2031, Oracle is positioning its high-performance infrastructure as the central control plane for global enterprise automation.

At the Oracle AI World Tour in London, Oracle announced groundbreaking agentic AI innovations for its AI Database. These new capabilities allow organizations to build and scale secure AI agents that interact directly with real-time business data. The launch aims to eliminate the risks and complexities of moving data between separate systems while providing stock exchange level robustness for production workloads.

A standout feature is the Oracle AI Database Private Agent Factory, a no-code environment for creating data-driven workflows. This platform enables business analysts to deploy specialized agents, such as deep research or structured analysis tools, without manual coding. These agents run in secure containers, ensuring that sensitive information remains behind the corporate firewall and is never shared with third party providers.

The company also introduced the Oracle Autonomous AI Vector Database, now in limited availability. This service combines the simplicity of a vector store with the full power of a converged database engine. Users can start on a free tier and upgrade with a single click as their production needs expand, supporting everything from JSON to spatial data in one place.

Security remains a primary focus with the introduction of Oracle Deep Data Security. This feature enforces precise access rules directly at the database level, preventing AI agents from accessing unauthorized information. It provides a robust defense against modern threats like prompt injection by centralizing security logic away from application code.

Oracle is also embracing open standards through innovations like Vectors on Ice. This allows AI Vector Search to read data directly from Apache Iceberg tables in data lakehouses. This feature bridges the gap between structured databases and massive data lakes to achieve unified intelligence across the entire enterprise ecosystem.

Industry analysts suggest these moves challenge the status quo of fragmented AI infrastructure. While competitors often require multiple specialized databases to achieve similar results, the Oracle Unified Memory Core keeps agent context in one transactional engine. This architectural choice reduces the latency and data staleness typically found in early multi-vendor AI stacks.

Available across multicloud and on-premises environments, these updates are ready for immediate developer use. Business leaders can explore these tools to activate their data for AI without learning new skills or moving assets. More details are available on the Oracle official blog.

Activating Enterprise Data for the AI Era

LONDON, March 24, 2026: Oracle has unveiled new agentic AI innovations for the Oracle AI Database to help organizations build and scale autonomous applications with mission-critical reliability. Announced at the Oracle AI World Tour, these tools allow AI agents to securely query and act on real-time business data without the need for external data movement. This architectural shift focuses on activating data directly within operational systems rather than simply storing it for separate analytics. The release introduces the Oracle AI Database Private Agent Factory, a no-code environment that empowers business analysts to create specialized agents such as the Database Knowledge Agent. Alongside this, the Unified Memory Core provides a persistent context for AI across multiple data formats including JSON, graph, and relational data. This native integration allows agents to maintain reasoning consistency across complex, multi-step workflows without the latency of external syncing.

Security-First AI Infrastructure

To combat the growing risks associated with generative AI, Oracle has implemented Deep Data Security to enforce specific access rules at the database level. This ensures that AI agents only interact with information authorized for the specific end-user, protecting against threats like prompt injection and unauthorized exposure. By moving security guardrails to the source of the data, Oracle provides a superior level of protection compared to application-tier security models. In a market where many providers require separate vector databases and complex orchestration, Oracle’s converged approach stands out. While competitors like Snowflake and Databricks often rely on fragmented toolchains and external synchronization, Oracle integrates vector search and agent logic into its existing engine. This consolidation provides the transactional robustness and high availability required for full-scale production workloads.

Embracing Open Standards

Oracle is also championing open standards with Vectors on Ice, which enables AI search on Apache Iceberg tables. This ensures that unified intelligence can be achieved across both traditional databases and external data lakes without requiring data migration. Additionally, support for the Model Context Protocol (MCP) allows third-party agents to interact securely with the database using standardized frameworks. Juan Loaiza, executive vice president at Oracle, stated that the next wave of enterprise AI will be defined by the ability to use AI in business-critical systems. He emphasized that by architecting AI and data together, Oracle helps customers deliver breakthrough innovations with stock exchange-level reliability. This release reinforces Oracle’s position as a leader in providing high-performance, secure data infrastructure for the modern enterprise. Learn more at the Oracle AI Database blog.