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For decades, core enterprise operations — from global supply chains and financial ledgers to billing engines — have relied on the performance and transactional integrity of Oracle databases. Today at Oracle AI World in Las Vegas, Google Cloud and Oracle are expanding our partnership across the AI stack — spanning AI compute and infrastructure (including making Google TPUs available through Oracle Cloud Infrastructure), enterprise data, and agentic AI — by deepening the integration between Oracle AI Database@Google Cloud, Google’s Agentic Data Cloud, and Gemini Enterprise.

By unifying Oracle’s mission-critical operational data and applications with Google Cloud’s data and AI ecosystem — including BigQuery, Gemini Enterprise, and Gemini models — enterprises can move beyond static analytics and build autonomous, agentic workflows. This architecture breaks down traditional data silos, giving agents direct, secure, and real-time access to the foundational enterprise data required to drive real-world action.

Foundational building blocks for agentic AI

To unlock production-grade agentic AI, enterprises need more than raw model capability — they need strong database connectivity, unified data governance, and rich semantic context working together as a cohesive foundation. First announced at Google Cloud Next ’26, the following core capabilities serve as the building blocks for building conversational intelligence, end-to-end governance, and autonomous agent environments on Oracle AI Database@Google Cloud:

  • Remote MCP Server with Data API: The Remote Model Context Protocol (MCP) Server for Oracle workloads — powered by the IAM-governed ExecuteSQL Data API — is now generally available. Together, they provide a unified, standardized interface that enables AI agents, Google Cloud services, and enterprise applications to securely run SQL and interact directly with Oracle databases. By resolving database credentials automatically through Secret Manager and enforcing Google Cloud IAM policies, this capability eliminates the need for custom natural-language-to-SQL middleware, bastion VMs, or plaintext passwords.

  • Knowledge Catalog integration: Google Cloud’s Knowledge Catalog integration, currently in preview, unifies enterprise metadata discovery and governance across Oracle databases and the broader Google Agentic Data Cloud. By automatically cataloging schemas and business metadata, organizations can enforce consistent access controls, track data provenance, and supply the rich semantic context that agents require to reason accurately over complex operational data. Click here to sign-up for this feature.

Gemini connector for Oracle AI Database@Google Cloud

Building directly on the foundational capabilities discussed above, we’re also announcing the preview of Gemini connector for Oracle AI Database@Google Cloud. With this native integration, business users and developers can interact directly with their Oracle databases on Oracle AI Database@Google Cloud using natural language queries right inside the Gemini app. Behind the scenes, Gemini automatically leverages Knowledge Catalog to discover, search, and ground responses strictly in the governed Oracle data each user is authorized to access — turning complex operational databases into an intuitive, conversational experience without compromising enterprise security. Click here to sign-up for this feature. 

Oracle in Gemini and Gemini in Oracle

Beyond direct database queries, Google Cloud and Oracle are connecting agentic workflows across the broader application layer — bringing Oracle data into Gemini Enterprise and embedding Gemini models natively inside Oracle applications:

  • Oracle in Gemini Enterprise: Available today, Oracle in Gemini Enterprise introduces conversational analytics, cross-cloud database connectors, and domain-aware agents — including the new Oracle AI Database Agent in Gemini Enterprise. Anchored by over 135 pre-built operational and developer skills and standardized MCP connectivity with zero plaintext credentials, enterprises can orchestrate multi-agent workflows across JD Edwards, E-Business Suite, NetSuite, and Oracle Fusion without custom SQL or brittle middleware.

  • Gemini models in Oracle applications: Concurrently, Gemini frontier reasoning is embedded directly into Oracle Fusion AI Agent Studio, Fusion Claw, Oracle Fusion Applications, and NetSuite — combining Oracle database intelligence with Gemini’s agentic capabilities to automate execution across transactional systems of record.

  • Partner-ready agentic blueprints: To help organizations rapidly deploy these governed workflows, Google Cloud and Oracle are collaborating with leading partners and global systems integrators — including Argano, Automus, Calfus, HCL, IBM, Infosys, LTM, Pythian, TCS, and Tessell — who provide pre-built operational skills, custom MCP connectors, and industry blueprints for production-grade agentic architectures.

Empowering choice with lakehouse, streaming, and federation

Every enterprise has distinct architectural priorities, latency thresholds, and governance requirements when connecting operational Oracle data to BigQuery and AI workloads. Rather than prescribing a one-size-fits-all integration pattern, Google Cloud and Oracle offer a complete spectrum of data unification choices so customers can select — or combine — the exact approach that best fits their workload and use case:

Bi-directional Google Cloud borderless Lakehouse and Oracle AI Lakehouse

Modern enterprise data architectures are increasingly multi-cloud and grounded in open standards. Building on our strategic partnership, Google Cloud and Oracle delivered a key innovation earlier this year to support cross-cloud data interoperability powered by open Apache Iceberg standard: integrating Google’s borderless Lakehouse with Oracle AI Lakehouse.

This work enabled the borderless Lakehouse to read Apache Iceberg tables created by Oracle AI Lakehouse across any public cloud or on-premises environment, leveraging catalogs and storage supported by both platforms.

Today, we are advancing it further by making it fully bi-directional with Oracle Access to Google Cloud borderless Lakehouse (preview). Now, Oracle Autonomous AI Database and Google’s borderless Lakehouse (powered by BigQuery and Managed Spark) can directly query and execute write/DML operations on shared Apache Iceberg tables in Google Cloud Storage, completely eliminating the need for ETL or data replication.

By utilizing a Lakehouse runtime REST catalog integrated into Oracle via DBMS_CATALOG, this bi-directional integration delivers an open-standards storage framework while empowering analytical teams (using BigQuery and Gemini) and operational teams (using Oracle SQL) to collaborate over a single, unified source of truth. You can click here to sign-up for this feature.

Sub-second CDC streaming with OCI GoldenGate

For organizations that prefer to continuously reflect mission-critical operational data inside BigQuery for high-scale analytics and AI, real-time change data capture (CDC) provides an immediate competitive edge. Through native OCI GoldenGate integration, CDC events are extracted directly from Oracle redo logs without touching table blocks or adding overhead to transactional workloads. Transactions stream securely across Google Cloud private networks straight into BigQuery managed tables, delivering sub-second analytical freshness to power Gemini, feature stores, and live business intelligence dashboards, while also streamlining migrations from on-premises environments to Oracle AI Database@Google Cloud.

BigQuery query federation with Oracle

When workloads call for on-demand operational visibility or infrequent cross-estate joins without moving data, BigQuery query federation with Oracle, in preview, enables real-time, in-place analytics directly against live Oracle databases. Using the EXTERNAL_QUERY function, data teams can execute native Oracle SQL directly from BigQuery queries, dashboards, and pipelines to retrieve live transactional records and join them easily with petabyte-scale BigQuery datasets. This zero-copy approach simplifies operations and lowers Total Cost of Ownership (TCO) by eliminating duplicate storage and ETL maintenance for ad-hoc lookups, intraday risk reporting, and live fraud detection. Please reach out to your Google Cloud account team if you want to sign-up for this feature.

Architectural option Mechanism Data Freshness and Latency Impact on Oracle OLTP Primary Use Vase
Bi-Directional Borderless Lakehouse Shared Apache Iceberg tables via Lakehouse runtime REST catalog (DBMS_CATALOG) and Glue Federation Real-time shared storage (zero copy) Zero (shared analytical storage layer) Multi-cloud open interoperability, unified BigQuery/Spark/Oracle SQL access without vendor lock-in
Real-Time CDC via OCI GoldenGate Continuous log-based streaming into BigQuery via Storage Write API Near real-time / Sub-second (< 1s) Zero (non-invasive extraction from redo logs) Mission-critical operational reflection in BigQuery, live AI feature stores, continuous ML and BI pipelines
BigQuery Query Federation (Preview) In-place query execution using EXTERNAL_QUERY via BigQuery Connection API On-demand / Live in-place

Variable (query-dependent)

 Infrequent ad-hoc querying, zero-copy lookups, cross-estate joins, regulatory reporting

 

Global expansion and public sector compliance

We continue to expand the global physical footprint and compliance boundaries of Oracle AI Database@Google Cloud to meet customers wherever they operate, with new regions and certifications.

New regions launched 

  • The europe-west12-a(Turin, Italy) and northamerica-south1-b (Queretaro, Mexico) regions are now live, joining global hubs including Milan, Iowa, São Paulo, Tokyo, Sydney, and Mumbai to deliver ultra-low-latency access and local data residency.

FedRAMP High certification 

  • At Oracle AI World, Oracle announced plans to pursue FedRAMP High certification for Oracle AI Database@Google Cloud. This initiative will allow US government and public sector entities to run regulated workloads using Oracle Exadata Database Service on dedicated infrastructure within Google Cloud Assured Workloads. 

  • Operating inside a FedRAMP High-authorized environment enables federal agencies to safeguard sensitive, unclassified data while co-locating Oracle workloads with existing applications and Gemini. Agencies can access live Oracle data with minimal latency, streamlined billing, and simplified procurement via Google Cloud Marketplace or certified partners — all within their compliance boundaries.

Building the agentic future

By combining Oracle’s proven transactional engine with Google’s Agentic Data Cloud, enterprise data transitions from passive systems of record into an active, intelligent operational engine. Whether unifying data through open Apache Iceberg lakehouses, streaming sub-second changes via GoldenGate, federating live queries from BigQuery, or conversing directly with governed Oracle data through Gemini, organizations now have the complete flexibility and security required to deploy autonomous AI agents at scale — driving predictive insights and automated actions across their most critical business processes.

Get started today

Oracle AI Database@Google Cloud is available directly through the Google Cloud Marketplace, allowing you to deploy using your existing Google Cloud commitments, unified billing, and Oracle Support Rewards.

To explore further, visit the Oracle AI Database@Google Cloud overview, dive into the official Oracle Database@Google Cloud Documentation, or reach out to your Google Cloud account team to schedule an architecture briefing.