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Oracle's Converged Database Strategy: Giving AI Agents Business Context to Drive Real Transactions

Generic AI models lack knowledge of a company's specific operations, customers and financial metrics. Oracle Database is positioning itself as a unified platform that equips AI agents with the business context needed to resolve real-world problems like stuck orders and delivery delays.

·2 min read
Same model, better answer: Enterprise data is what turns generic AI into a business tool
Same model, better answer: Enterprise data is what turns generic AI into a business tool

While large language models can generate SQL queries, they operate without understanding the particulars of any given organization—its order history, customer relationships or how revenue flows through the business. Oracle Database is working to bridge that gap by furnishing AI agents with the contextual knowledge required to take action on actual transactions that have stalled in the workflow.

This evolution introduces fresh concerns around operational complexity, vendor dependency and data protection, particularly as AI systems begin executing operations against live business data. Oracle's response centers on a unified database architecture that consolidates vectors, graphs, documents and transactional records within a single system, according to Hasan Rizvi, executive vice president of product and research, database engineering, at Oracle.

If you ask a generic AI assistant, you get a generic answer. If you ask an AI assistant that knows your business, you get a much better answer. Where is the stock? How can I fix the transaction? How can I expedite the delivery?

Hasan Rizvi, Oracle

Convergence over specialized databases

The speed at which AI enables teams to deploy new applications and databases has created widespread data fragmentation across enterprises. While some argue for purpose-built databases tailored to specific use cases, Rizvi contends that introducing new data types within Oracle's existing engine allows organizations to inherit decades of proven reliability, protection and performance characteristics.

We've been working on this engine for 40 years, which is highly available, highly secure, highly scalable, highly performant. If you build a new data type or a new workload in the same engine, you get all that benefit, multiple decades' worth of investment.

Hasan Rizvi, Oracle

Deployment flexibility forms the second pillar of Oracle's strategy. The platform operates in on-premises environments, through Oracle Cloud@Customer and across multiple public cloud providers. At Oracle AI World, the company announced expanded support for Iceberg within its Autonomous AI Lakehouse offering, Rizvi noted.

Security represents the area where AI integration carries the highest stakes. Oracle's Deep Data Security framework establishes access controls at the database layer itself, ensuring that user identity and permissions accompany every request originating from an AI agent or AI-generated application.

https://www.youtube.com/embed/QABxptzK5WA?feature=oembed

Before, it took a lot of time and effort to find vulnerabilities to exploit them. Now, with AI, it's made it very easy to reverse engineer fixes for attackers to attack.

Hasan Rizvi, Oracle

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