# Oracle adds a semantic layer to ground AI agents in the business context

DevFeed: [Oracle adds a semantic layer to ground AI agents in the business context](<https://devfeed.tech/articles/oracle-adds-a-semantic-layer-to-ground-ai-agents-in-the-business-context-100449.md>)

Original publisher: [Read original article](<https://www.infoworld.com/article/4240304/oracle-adds-a-semantic-layer-to-ground-ai-agents-in-the-business-context.html>)

Author: Anirban Ghoshal

Published: 2026-10-26T12:32:30Z

Content type: news

Language: en

Sources: [InfoWorld](<https://devfeed.tech/sources/infoworld.md>)

Topics: [semantic-layer](<https://devfeed.tech/topics/semantic-layer.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [business-intelligence](<https://devfeed.tech/topics/business-intelligence.md>), [Apache Iceberg](<https://devfeed.tech/topics/apache-iceberg.md>), [Oracle Database](<https://devfeed.tech/topics/oracle-database.md>), [CRUD](<https://devfeed.tech/topics/crud.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-reliability](<https://devfeed.tech/tags/ai-reliability.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [analytics-artificial-intelligence](<https://devfeed.tech/tags/analytics-artificial-intelligence.md>), [annotations](<https://devfeed.tech/tags/annotations.md>), [apache-iceberg](<https://devfeed.tech/tags/apache-iceberg.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [autonomous-ai-lakehouse](<https://devfeed.tech/tags/autonomous-ai-lakehouse.md>), [business](<https://devfeed.tech/tags/business.md>), [business-context](<https://devfeed.tech/tags/business-context.md>), [context](<https://devfeed.tech/tags/context.md>), [enterprise-data](<https://devfeed.tech/tags/enterprise-data.md>), [excel](<https://devfeed.tech/tags/excel.md>), [forecasting](<https://devfeed.tech/tags/forecasting.md>), [generate](<https://devfeed.tech/tags/generate.md>), [language-en](<https://devfeed.tech/tags/language-en.md>), [layer](<https://devfeed.tech/tags/layer.md>), [oracle](<https://devfeed.tech/tags/oracle.md>), [semantic](<https://devfeed.tech/tags/semantic.md>), [semantic-layer](<https://devfeed.tech/tags/semantic-layer.md>), [storytype-news](<https://devfeed.tech/tags/storytype-news.md>)

## AI overview

Oracle's Autonomous AI Lakehouse is adding a Business Semantic Layer that combines catalog and SQL annotations with AI-suggested analytic views to model enterprise dimensions, measures, hierarchies, calculations, and KPIs. The reusable models are intended to give Select AI and other query, retrieval, and analytics features consistent business definitions; Oracle says availability is expected within 12 months. A new Business Simulation Engine integrates Essbase for forecasting, what-if analysis, write-back, hierarchy rollups, calculations, allocations, natural-language questions, reports, and Excel analysis over Oracle database or object-storage data. Oracle is also expanding SQL support for creating and modifying Apache Iceberg tables and improving access through catalogs. Tradeoffs include the ongoing governance needed to keep semantic models accurate, integration with existing planning workflows, and possible reliance on proprietary catalogs or semantic layers despite Iceberg's portability. Release timing for the simulation engine and expanded Iceberg capabilities is unspecified.

## Source excerpt

Oracle is expanding its Autonomous AI Lakehouse with a Business Semantic Layer designed to give AI agents a consistent understanding of enterprise data, a move aimed at improving the accuracy of AI-generated insights and helping organizations scale agentic applications across business functions. To ground AI agents in business context, the Business Semantic Layer uses catalog annotations, SQL annotations imported from Oracle AI Database, and AI-suggested analytic views to create reusable models of enterprise data that capture dimensions, measures, hierarchies, calculations, and key performance indicators (KPIs), Oracle said in a statement. These models can then be used across Oracle's natural-language querying, data retrieval, and analytics capabilities, including Select AI, to help ensure that queries and insights are based on consistent business definitions. Oracle said that the new capabilities will be available within the next 12 months. Alongside the new layer, at the company's annual AI World conference, Oracle also launched a Business Simulation Engine to help enterprises evaluate business scenarios and make more informed planning decisions by integrating Oracle Essbase into the Autonomous AI Lakehouse. Beyond forecasting and what-if analysis, the engine supports write-back, dynamic hierarchy rollups, advanced calculations, and allocations, the company said. Enterprise users can also ask questions in natural language, generate simulation reports, and perform Excel-based analysis on data stored in Oracle databases or in object storage via the new engine, it added. Additionally, Oracle is expanding Apache Iceberg support in the lakehouse to let customers create, populate, update, and delete Iceberg tables using SQL, while improving performance and functionality when accessing Iceberg data through external catalogs or its AI Data Catalog. Business context could improve AI reliability, support scale-up That ability to ground AI agents in business context will hel