Oracle launches AI database agentic innovations for business data

Oracle has launched new agentic AI innovations for Oracle AI Database that will help customers rapidly build, deploy, and scale secure agentic AI applications that are suitable for full-scale production workloads. Oracle AI Database architects agentic AI and data together across operational databases and analytic lakehouses. This enables AI agents to securely access real-time enterprise data wherever it resides.

The agents can use business data with LLMs trained on public data to provide business insights. Customers can choose AI models, agentic frameworks, open data formats, and deployment platforms. Additionally, customers running on Oracle Exadata further benefit from Exadata Powered AI Search. Therefore, this enables agentic AI at the highest scale with accelerated AI queries for high-volume, multi-step agentic workloads.

Juan Loaiza, Executive Vice President, Oracle Database Technologies, Oracle, 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.

“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. This is achieved with stock exchange-level robustness in every leading cloud and on-premises.”

Innovate faster with AI designed for data

With agentic AI capabilities architected for data, Oracle AI Database helps eliminate the need to build and maintain data-movement pipelines. These pipelines add complexity and security risk, and may produce worse outcomes. New capabilities include:

• Oracle Autonomous AI Vector Database: This provides the simplicity of a vector database with the full power of Oracle AI Database. It enables developers and data scientists to easily build vector-powered applications using intuitive APIs and an easy-to-use web interface. Built on top of Oracle Autonomous AI Database, it has an easy-to-use developer experience with enterprise-grade security, reliability, and scalability.
• Oracle AI Database Private Agent Factory: This enables business analysts and domain experts to build and deploy data-driven agents and workflows. The AI Database Private Agent Factory provides a no-code AI agent builder that runs as a container in public clouds or on-premises. It maintains data security by enabling customers to build, deploy, and manage AI agents without having to share data with third parties.
• Oracle Unified Memory Core: This lets users store context for AI agents in a single system. It uniquely enables low-latency reasoning across vector, JSON, graph, relational, text, spatial, and columnar data in one converged engine. Oracle says this is managed with consistent transactions and security.

Minimise AI data risk

Oracle AI Database helps customers safeguard data from external attacks, insider misuse, accidental disclosure, and unintended exposure to LLMs. This is across multicloud, hybrid, and on-premises environments. New capabilities include:

• Oracle Deep Data Security – implements end-user-specific data access rules in the database. Each end-user or AI agent acting on behalf of an end-user can only see the data that the end-user is allowed to see. Using sophisticated persona and function-based rules provides unique end-user data security capabilities to protect against new AI-era threats.
• Oracle Private AI Services Container – enables customers with stringent security requirements to run private instances of AI models. This is managed while avoiding the sharing of data with third-party AI providers or sending data outside of the firewall. In addition, it helps mitigate performance bottlenecks by allowing customers to securely offload compute-intensive AI tasks.

• Oracle Trusted Answer Search – provides enterprises with an accurate, testable, and deterministic way to use AI to provide answers to end-users. Instead of directly using an LLM to answer an end-user question, Trusted Answer Search uses AI Vector Search to match the question to a previously created report. This helps mitigate the risk that probabilistic LLMs may occasionally hallucinate or misunderstand a query.

Open standards and frameworks

The solution runs in all leading cloud providers, in hybrid deployments, and is available on-premises. Oracle AI Database gives customers the flexibility to choose the AI model and application-tier agentic framework that best fits their needs. They can build, deploy, and run agentic AI applications using open standards and data formats. New capabilities include:

• Oracle Vectors on Ice – provides customers with native support for vector data that is stored in Apache Iceberg tables. AI Vector Search can read vector data directly from Iceberg tables. It creates vector indexes to accelerate vector search, and automatically update these indexes as the underlying vector data changes.
• Oracle Autonomous AI Database MCP Server – enables external AI agents and MCP clients to securely access Autonomous AI Database and its capabilities without custom integration code or manual security administration. It complements the Oracle SQLcl MCP Server for Oracle AI Database, available via the Oracle SQL Developer VS Code extension.

Enterprise Times: What this means for businesses

Oracle made several announcements during its Oracle AI World Tour in London’s ExCeL Centre. These included innovations with Oracle Health Clinical AI agent, new agentic apps for supply chain and finance, and the AI data platform for unified data management. The new agentic AI capabilities designed for business data accelerate enterprise innovation and help defend enterprises from AI-era threats.

As a result, Oracle has simplified agentic AI for business users by architecting it into its AI Database. It is expected to provide consistency and simplicity—with enterprise-grade security, resiliency and scalability for every agentic workload.

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