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The Missing Layer for Reliable AI Agents: Business Ontologies

Enterprises are investing billions of dollars in AI agents and infrastructure to transform business processes. However, we are seeing limited success in real-world applications, often due to the inability of agents to truly understand business data policies and processes.

While we manage the integrations well with technologies like API management, model context protocol (MCP) and others, having agents truly understand the “meaning” of data in the context of a given business is a different story.enterprise data is mostly siloed into disparate systems in structured and unstructured forms and needs to be analyzed with a domain-specific business lens.

As an example, the term “customer” may refer to a different group of people in a Sales CRM system, compared to a finance system wich may use this tag for paying clients.One department might define “product” as a SKU; another may represent it as a “product” family; a third as a marketing bundle.

Data about “product sales” thus varies in meaning without agreed upon relationships and definitions. For agents to combine data from multiple systems, they must understand different representations. Agents need to know what the data means in context and how to find the right data for the right process.Moreover, schema changes in systems and data quality issues during collection can lead to more ambiguity and inability of agents to know how to act when such situations are encountered.

furthermore, classification of data into categories like PII (personally identifiable details) needs to be rigorously followed to maintain compliance with standards like GDPR and CCPA.This requires the data to be labelled correctly and agents to be able to understand and respect this classification. Hence we see that building a cool demo using agents is very much doable – but putting into production working on real business data is a different story altogether.

the Ontology-Based Source of truth

Building effective agentic solutions requires an ontology-based single source of truth.Ontology is a business definition of concepts, their hierarchy and relationships. It defines terms with respect to business domains,can help establish a single-source of truth for data and capture uniform field names and apply classifications to fields.

An ontology may be domain-specific (healthcare or finance), or organization-specific based on internal structures. Defining an ontology upfront is time consuming, but can help standardize business processes and lay a strong foundation for agentic AI.

Ontology may be realized using common queryable formats like triplestore. More complex business rules with multi-hop relations could use a labelled property graphs like Neo4j. These graphs can also help enterprises discover new relationships and answer complex questions. Ontologies lay the groundwork for robust AI agent functionality.