What happened
Snowflake is launching Business Ontology, a tool that allows enterprises to define and approve business concepts, assign owners to those definitions, and link them directly to the underlying tables and views. This should give enterprise AI agents access to authoritative business definitions, such as what constitutes net revenue, rather than relying on competing interpretations of the same data across different systems.
Business Ontology builds on the foundation of Horizon Catalog, which received a boost in June with the launch of Horizon Context, a set of semantic and metadata management capabilities.
Business Ontology allows enterprises to define and approve business concepts, assign owners to those definitions, and link them directly to the underlying tables and views.
This should give enterprise AI agents access to authoritative business definitions, such as what constitutes net revenue, rather than relying on competing interpretations of the same data across different systems.
Business Ontology also offers enterprises a way to define and approve business meanings and relationships that have not been formally documented.
Typically, enterprises subscribed to Snowflake have had to rely on Cortex Sense, Snowflake’s AI-powered business context engine, to infer these meanings from available metadata and usage patterns, which may not always reflect the intended business definitions.
Why it matters
The launch of Business Ontology is significant because it addresses a major challenge in development: ensuring that agents use business-validated meanings rather than relying solely on AI-generated inferences. This can simplify agent development, reduce inconsistencies between agents interpreting the same business data differently, and make it easier to develop and maintain agentic applications as business requirements evolve.
The launch of Business Ontology is significant because it addresses a major challenge in development: ensuring that agents use business-validated meanings rather than relying solely on AI-generated inferences.
This can simplify agent development, reduce inconsistencies between agents interpreting the same business data differently, and make it easier to develop and maintain agentic applications as business requirements evolve.
For developers, Business Ontology can reduce the effort and time required to stitch context for agents or agentic applications as they no longer have to bake business logic into every agent they build.
Primarily because agents can draw on the centrally maintained definitions in Business Ontology, allowing developers to reuse the same business context across multiple agents and applications.
This, in turn, could also reduce inconsistencies between agents interpreting the same business data differently, while making it easier to develop and maintain agentic applications as business requirements evolve.
Interactive Mechanism: How It Actually Works
Explore the underlying technology behind this development interactively.
crm_get_transaction(id='4092').An agent must create a draft calendar event for Tuesday at 2 p.m. Which evidence would establish the requested result?
What to watch next
The impact of Business Ontology on development and deployment will be significant, the ability of enterprises to establish a common set of business definitions is crucial, and the challenges of keeping business definitions current are substantial.
The impact of Business Ontology on development and deployment will be significant.
The ability of enterprises to establish a common set of business definitions is crucial.
The challenges of keeping business definitions current are substantial.
The potential for Business Ontology to simplify agent development and reduce inconsistencies between agents interpreting the same business data differently is substantial.
The ability of Business Ontology to make it easier to develop and maintain agentic applications as business requirements evolve is significant.