How effective are semantic hubs in moving agentic AI forward?

The focus of enterprise AI initiatives is shifting from storing, processing, and moving data to ensuring data means the same thing wherever it’s used. This is vital if an LLM is to understand the nuances and specifics of an individual business.

Walmart’s recent announcement that it’s ending its partnership with OpenAI to power shopping through ChatGPT is a case in point. Relying on the LLM to scrape Walmart’s product data and then infer meaning from that led to hallucinations and poor customer experience, resulting in conversion rates three times lower than shoppers using Walmart’s own website. Had the AI agent been grounded in Walmart’s actual business logic developed over years, then the results might have been very different.

Semantic hubs that provide a centralized architecture translating raw data into consistent and clear business concepts are key components in powering effective agentic AI deployments. They mitigate the risks of semantic drift where an LLM’s understanding of concepts and terms is fluid, and changes over time. However, businesses don’t operate in a vacuum and need to exchange data with suppliers, customers, regulators, and financial institutions. Semantic hubs on their own may provide a point of failure in these instances as definitions and understanding will vary across organizations. As commerce moves toward more autonomous agentic systems, this is a problem.

A shared language

McKinsey predict AI agents could handle between $3 and $5 trillion of global commerce by 2030. The economic and business rationale for moving to a more agentic world is compelling and, although still in its early stages, the technology that will power this revolution is developing rapidly. MCP, Agent2Agent (A2A), and other open standards offer protocols for agents to communicate with each other and pull in data as needed. However, a missing building block is a common language that allows dispersed agents to consistently and accurately infer meaning from the data they use.

The Open Semantic Interchange (OSI) initially developed by Salesforce and Snowflake can be seen as a universal mental model for data, ensuring every AI agent perceives business definitions with the same precision and intent as a human expert, regardless of which system they navigate. “If successful, OSI has the power to fundamentally reshape the competitive landscape by commoditizing the definition of a semantic model,” says Brad Shimmin, VP practice lead, data and analytics at tech analyst The Futurum Group. “Vendors will no longer be able to lock in customers with proprietary metric languages. Instead, they’ll need to compete on the execution of semantics, differentiating on performance, caching efficiency, security, and the sophistication of their AI integrations.”

Hurdles on the road to technical sovereignty

While the OSI initiative was only launched in September last year, major vendors including Cloudera, Databricks, Instacart, and ThoughtSpot have now joined founding members Salesforce and Snowflake in developing the standard. However, many CIOs are concerned that without wider cooperation from other enterprise software providers like Microsoft (Power BI) and SAP, there’s a risk it won’t build the momentum needed for true interoperability across industries. The rapid adoption of MCP by competing vendors offers hope that more companies will join the initiative as they realize common and open standards are required to build effective agentic systems.

Also, OSI specification is still in its early development phase, so committing live data assets to tools still in beta isn’t viable in the short term. This is likely to change over the coming months as leading vendors incorporate OSI and case studies emerge.

Another essential driver of adoption will be support from industry groups that have vested interests in seeing safe and reliable agentic systems be deployed through their sectors. The banking and medical sectors, for instance, rely on agreed languages and definitions to prevent financial fraud and health risks. Trevor Hall, chief architect at Tableau Salesforce and an OSI contributor says that while OSI wouldn’t necessarily define specific domain models such as medicine, he’d hope that industry would actually lean on OSI to define its models.

OSI next steps

Snowflake is positioning itself as a primary custodian of the OSI standard, and with around 20% of the cloud data warehousing market, it has the potential to drive adoption. Salesforce is the other key founding member that sees OSI as a core element of its Agentforce fleet of AI agents. Alongside this, Phase 2 of expanding the OSI ecosystem will continue through the remainder of 2026, with plans to bring native import and export buttons for OSI models to over 50 different data platforms.

Things are moving quickly in the agentic space and the underpinning infrastructure is taking shape. Now’s the time to make sure your data is ready for this revolution, and OSI could be a key element in your planning.