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 businesis 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.sAs an example, the term “customer” may refer to a different group of people in a Sales CRM system, compared to a finance system which may use this tag for paying clients. One department might define “product” as a SKU; another may represent 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 information) 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 …
