Key points
- Oracle positions its Autonomous AI Lakehouse as a decision backbone for AI rather than just analytics.
- The system mounts external catalogs like Databricks Unity and databases like Postgres.
- Real-time access is prioritized because AI agents request data on demand.
Oracle executives stated that their Autonomous AI Lakehouse now serves as the decision backbone for artificial intelligence, moving beyond traditional analytics roles. Nipun Agarwal, senior vice president of software engineering at Oracle, made this claim during an interview with SiliconANGLE Media.Oracle says its new lakehouse lets AI tools check data from many sources instantly.Oracle’s semantic layer mounts external catalogs to enable in-place querying for AI agents. He described the platform as an open layer that sits across an enterprise’s existing data estate rather than replacing it. This shift reflects a broader modernization push by the company.
The claim
Oracle announced a business semantic layer and broader Apache Iceberg support at an October event. According to Agarwal, the system generates table and column descriptions that apply to non-Oracle sources as well.reported The approach rests on a catalog that can mount other catalogs, such as Databricks Inc.’s Unity Catalog, along with Postgres and MySQL databases. This allows data to be queried in place rather than copied.
The response
Agarwal told theCUBE that Oracle does not view the offering as competing with other lakehouses. He stated that enterprises will likely use multiple data sources and multiple lakehouses. The company aims to provide real-time access to data across platforms to support AI agents. This contrasts with scheduled refreshes of copied data, which Agarwal said can fall short when agents request information on demand.
What the evidence means
The material suggests that real-time access is becoming essential as software agents become the main consumers of corporate data. Oracle’s Lake Cache speeds up queries by moving the most-used Iceberg tables into Exadata’s flash cache. The company argues that because AI consumers are not necessarily human, the need for real-time access is significantly higher than in previous analytics workflows.
In practice
Users can query data scattered across clouds, formats, and rival systems without copying it. The system mounts external catalogs and databases, allowing AI agents to request data on demand. This addresses the limitations of scheduled data refreshes. However, the availability of these features depends on Oracle’s Autonomous AI Lakehouse and its integration with Exadata flash cache.
Why it matters
- Enterprises can avoid the cost and latency of copying data by querying it in place.
- AI agents gain access to real-time data from multiple sources, including non-Oracle systems.
- The semantic layer provides consistent descriptions for tables and columns across different platforms.
- Organizations can maintain multiple lakehouses while using Oracle as a unified access point.
Topics: Oracle, AI agents, data lakes, semantic layer, Apache Iceberg
Updated
Then / now / next
Lakehouses were primarily used for running traditional analytics and reporting.
Oracle positions its lakehouse as a real-time decision backbone for AI agents.
Watch for broader adoption of shared semantic layers across multiple data platforms.
Story timeline
- Then Lakehouses were primarily used for running traditional analytics and reporting.
- 04 Nov 2026 Customers are pulling data back into the Oracle database as agents demand real-time access
- Now Oracle positions its lakehouse as a real-time decision backbone for AI agents.
- Next Watch for broader adoption of shared semantic layers across multiple data platforms.
Ask the article
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Your choice stays on this device. No public tally.Terms in this story
Semantic layer
A system that provides consistent descriptions and access to data across sources
Apache Iceberg
An open table format for large-scale data lakes
Exadata
Oracle’s engineered system for database performance and caching
Questions readers ask
Does Oracle’s lakehouse replace other data platforms?
No, Oracle executives stated it sits across existing data estates and supports multiple lakehouses.
How does the system handle non-Oracle data sources?
It mounts external catalogs and databases, querying data in place without copying it.
Why is real-time access important for AI agents?
Agents request data on demand, making scheduled refreshes of copied data insufficient.
Connected stories
Reading receipt
- Oracle positions its Autonomous AI Lakehouse as a decision backbone for AI rather than just analytics.
- The system mounts external catalogs like Databricks Unity and databases like Postgres.
- Real-time access is prioritized because AI agents request data on demand.
Still open: How does the performance compare to native queries on other platforms?
Still open: What are the specific costs for mounting external catalogs?