For example, a user could ask a question in Gemini Enterprise such as, \u201cWhich products are most at risk of stockouts next quarter across North America, and what are the likely revenue implications?\u201d Gemini Enterprise passes the request to the Oracle AI Database Agent, which interprets it and returns an answer grounded in trusted business context.<\/p>\n
What makes this powerful is that the Oracle AI Database Agent does more than just process SQL queries, it takes intent from Gemini Enterprise and applies semantic understanding and governance guardrails to responses. The result is typically higher quality responses while maintaining your data security, delivered at greater efficiency. This is especially important for organizations with data distributed across multiple Oracle databases and environments.<\/p>\n
Security and privacy are foundational to this approach. Queries made through the Oracle AI Database Agent are processed by Oracle AI Database using the new Oracle Deep Data Security feature, a database-native, identity-aware access control system designed for agentic AI. Deep Data Security helps propagate end-user and agent identity to the database at runtime so access policies can be implemented centrally and consistently at the data layer. With fine-grained controls at the row, column, and cell level, Oracle AI Database helps ensure users and agents can access only the data they are authorized to see.<\/p>\n
Beyond natural language queries, developers can use the Oracle AI Database Agent to power automated, multi-step workflows<\/a> that combine data access, analysis, and action. With full Agent-to-Agent (A2A) compatibility, developers can use the Agent Development Kit (ADK) to integrate the agent with Gemini Enterprise Agent Platform. This approach provides secure, programmatic access Oracle data and empowers teams to create bespoke automations, such as data retrieval, analysis, visualization, and downstream actions, by orchestrating the Oracle AI Database Agent alongside other agents. For example, a developer could build a workflow where an agent identifies a supply chain risk, calls the Oracle AI Database Agent to retrieve relevant operational data, analyzes the impact, and triggers a follow-up action all without manual queries.<\/p>\n Expanding Oracle AI Database@Google Cloud<\/p>\n We are also highlighting strong momentum behind Oracle AI Database@Google Cloud, with ongoing expansion in regions, services, and integrations. This jointly-operated managed service enables organizations to run Oracle AI Database on Google Cloud with enterprise-grade performance, availability, and security, while connecting to the Google Cloud services they already use.<\/p>\n Leading organizations including Banco Actinver<\/a> and Worldline<\/a> are choosing Oracle AI Database@Google Cloud to modernize their data platforms and accelerate innovation.<\/p>\n \u201cThis approach combines the flexibility of a cloud partner like Google with the power and simplicity of Exadata, as if we had our own data center, but in the cloud.\u201d- Arni Smit,\u00a0Director Software Engineering, Integration and Payment Platform, Worldline<\/p>\n Updates include:<\/p>\n Expansion to 15 regions<\/a>: Helps more organizations deploy latency-sensitive workloads closer to users, address data residency requirements, and scale consistently across Google Cloud regions. With additional regions like Mexico and Turin coming next, this expansion helps deliver higher availability and lower latency for your mission-critical workloads across the globe.<\/p>\n Oracle GoldenGate Service integration<\/a> (general availability expected later in CY26): Enables real-time, low-impact data movement to simplify migrations of on-premises Oracle AI Databases to Oracle AI Database@Google Cloud. It also integrates with BigQuery so you can analyze operational data stored in Oracle AI Databases with Google Cloud analytics services, enabling BigQuery\u2019s machine learning capabilities to run against real-time data Oracle data.<\/p>\n These updates are complemented by deeper integrations across Google Cloud data and AI services, with new capabilities that make it easier to bring Oracle data into AI workflows:<\/p>\n Remote Model Context Protocol (MCP) integration: Provides standardized, secure interfaces for AI agents and applications in Google Cloud to access and interact with Oracle Database, reducing integration complexity for AI-driven workflows. Check your organization\u2019s data and privacy policies before connecting to MCP.<\/p>\n![\"The](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-10-112245-1024x687.png\")
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