Oracle Adds Business Semantic Layer to Ground AI Agents in Enterprise Data Context

Oracle Introduces Business Semantic Layer for AI Agents

Oracle announced a major expansion to its Autonomous AI Lakehouse with the introduction of a Business Semantic Layer, a capability designed to ground AI agents in business context and provide them with consistent understanding of enterprise data. The new layer uses catalog annotations, SQL annotations imported from Oracle AI Database, and AI-suggested analytic views to create reusable models that capture dimensions, measures, hierarchies, calculations, and key performance indicators (KPIs).1

1

These models integrate across Oracle's natural-language querying, data retrieval, and analytics capabilities, including Select AI, to ensure queries and insights align with consistent enterprise data definitions. Oracle stated the new capabilities will be available within the next 12 months.1

1

Expanded Support for Iceberg Data and Business Simulation Engine

Alongside the Business Semantic Layer, Oracle launched expanded support for Iceberg Data, allowing customers to create, populate, update, and delete Iceberg tables using familiar SQL while improving performance when accessing data through external catalogs or its AI Data Catalog.1

1

The company also introduced a Business Simulation Engine that integrates Oracle Essbase into the Autonomous AI Lakehouse, enabling enterprises to evaluate business scenarios through multidimensional simulations and what-if analyses.2

2

The engine supports write-back, dynamic hierarchy rollups, advanced calculations, and allocations, while allowing users to ask questions in natural language and generate simulation reports on data stored in Oracle databases or object storage.1

1

Real-World Impact on AI Reliability and Decision-Making

The ability to ground AI agents in business context will help data professionals and developers build more reliable AI and analytics workflows by using approved business metrics and definitions, reducing time spent investigating and reconciling inconsistent results. Amit Kumar Jena, AI development head at Kanerika, noted that his firm implemented something similar for a global pharma company, modeling 40-plus entities and standardizing 85-plus KPIs, which resulted in investigation and reconciliation time falling by about 70%.1

1

These improvements in reliability will help CIOs scale agentic applications across business functions and rely on AI-driven decision-making with greater confidence, according to Manoj Chandra Jha, principal analyst at Nord-IQ Research.1

1

Liberty Energy Adopts Oracle's Unified Data Foundation

Liberty Energy, a North American energy services company serving oil and natural gas producers, is using Oracle Autonomous AI Lakehouse to unify financial, operational, supply chain, and workforce data across its multicloud environments.2

2

This unified data foundation supports AI-driven capabilities, analytics, and business simulations. Oracle Autonomous AI Lakehouse is available across Oracle Cloud Infrastructure, Amazon Web Services (AWS), Microsoft Azure, Google Cloud, Oracle Exadata Cloud@Customer, and OCI Dedicated Region.2

2

Streamlined Data Architecture and Reduced Data Movement

The Business Simulation Engine has potential to accelerate decision-making by bringing analytics and planning capabilities together within the lakehouse. Pareekh Jain, principal analyst at Pareekh Consulting, explained that by bringing analytics and planning closer together, the engine could help finance teams and business planners run what-if scenarios using enterprise data without repeatedly moving it between separate systems.1

1

For CIOs, this could simplify data architecture and accelerate decisions, while developers could spend less time building integrations between analytics and planning tools.1

1

The expanded Apache Iceberg support allows data engineers to work with a single copy of data using SQL while retaining flexibility to use Oracle alongside platforms such as Apache Spark, Snowflake, or Databricks on the same tables, reducing data duplication and associated storage and processing costs.1

1

Governance Challenges and Implementation Considerations

The Business Semantic Layer will require enterprises to define and maintain business metrics, relationships, and rules to ensure AI agents interpret data consistently. This creates an additional governance responsibility for CIOs, as incomplete or outdated models could still produce inconsistent AI-generated insights.1

1

Oracle's updates also include AI Data Catalog, which provides a holistic view of enterprise data by allowing customers to mount external catalogs, Oracle and non-Oracle databases, Iceberg tables, applications, and data in object storage without duplicating data.2

2

Data Studio enables teams to discover, access, prepare, analyze, and manage lakehouse data while streamlining integration and building transformation pipelines.2

2