Oracle expands Oracle AI Database with new agentic AI tools

Oracle has announced new agentic AI capabilities for Oracle AI Database, presenting them as tools for building, deploying, and scaling production-grade AI applications that work with business data across operational databases and analytic lakehouses. The company says the new features are available across multicloud and on-premises environments.

According to Oracle, the announcement concerning Oracle AI Database centres on bringing AI and data together within the database so that agents can securely access real-time enterprise data where it resides. Oracle also says customers can choose AI models, agentic frameworks, open data formats, and deployment platforms, while Oracle Exadata users can use Exadata Powered AI Search for high-volume, multi-step agentic workloads.

Oracle’s new product set includes Oracle Autonomous AI Vector Database, which the company says is intended to simplify vector-based application development while preserving the broader database features of Oracle AI Database. Oracle says the service is available in limited capacity through the Oracle Cloud free tier or a low-cost developer tier, with one-click upgrade to full capabilities as requirements expand.

The company also introduced the Oracle AI Database Private Agent Factory, described as a no-code agent builder that can run in public clouds or on-premises without requiring customers to share data with third parties. Oracle says the service includes pre-built agents such as a Database Knowledge Agent, a Structured Data Analysis Agent, and a Deep Data Research Agent. Oracle Unified Memory Core was also announced as a way to store context for AI agents across vector, JSON, graph, relational, text, spatial, and columnar data, all in a single engine with consistent transactions and security.

A separate part of the announcement focuses on what Oracle describes as AI data risk reduction. Oracle says Deep Data Security applies end-user-specific access rules within the database, so that each user or AI agent acting on a user’s behalf can only see the data the user is allowed to access.

Besides the Oracle AI Database, Oracle also announced Private AI Services Container for customers that want to run private model instances without sharing data with third-party AI providers, including in air-gapped environments. Trusted Answer Search was presented as a method for providing answers based on previously created reports rather than relying directly on large language model responses.

Open standards and interoperability form another part of Oracle’s pitch. Oracle says Vectors on Ice adds native support for vector data stored in Apache Iceberg tables, enabling unified search across database and data-lake content. Oracle also announced an Autonomous AI Database MCP Server to allow external AI agents and MCP clients to access Autonomous AI Database capabilities without custom integration code or manual security administration.

Juan Loaiza, executive vice president of Oracle Database Technologies, said: ‘The next wave of enterprise AI will be defined by customers’ ability to use AI in business-critical production systems to safely deliver breakthrough innovations, insights, and productivity.’ He added: ‘With Oracle AI Database, customers don’t just store data, they activate it for AI. By architecting AI and data together, we help customers quickly build and manage agentic AI applications that can securely query and act on real-enterprise data with stock exchange-level robustness in every leading cloud and on-premises.’

Steven Dickens, CEO and principal analyst at HyperFRAME Research, said: ‘In the era of agentic AI, a unified memory core is essential for agents to maintain context across diverse data types, such as vector, JSON, graph, columnar, spatial, text, and relational, without the latency or staleness of external syncing.’

Dickens added: ‘Only Oracle AI Database delivers this in a single, mission-critical engine with concurrent transactional and analytical processing, high availability, and ironclad security, enabling real-time reasoning over live business data. Organisations without this foundation will struggle with fragmented, unreliable agents, while those leveraging Oracle gain a decisive edge in scalable AI deployment.’

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