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 ma [...]
Semantic intelligence is a critical element of actually understanding what data means and how it can be used.Microsoft is now deeply integrating semantics and ontologies into its Fabric data platfor [...]
OpenAI introduced a new paradigm and product today that is likely to have huge implications for enterprises seeking to adopt and control fleets of AI agent workers.Called "Workspace Agents," [...]
Microsoft today announced the general availability of Agent 365 and Microsoft 365 Enterprise 7, two products designed to bring security and governance to the rapidly growing population of AI agents op [...]
Artificial intelligence agents powered by the world's most advanced language models routinely fail to complete even straightforward professional tasks on their own, according to groundbreaking re [...]
In 2026, data engineers working with multi-agent systems are hitting a familiar problem: Agents built on different platforms don’t operate from a shared understanding of the business. The result isn [...]
Remember this Quora comment (which also became a meme)?(Source: Quora)In the pre-large language model (LLM) Stack Overflow era, the challenge was discerning which code snippets to adopt and adapt effe [...]