AI has entered the enterprise but not as a single all-knowing assistant managing everything. Instead, companies are realizing that generalist AI falls short in specialist environments and a new trend is emerging: domain-specific AI agents and assistants that deeply understand the workflows, terminology, data structures, and logic of a particular business function. These digital teammates know what your enterprise data teams need. Generalist AI tools often misinterpret requests, pull from the wrong data, or generate outputs that are directionally correct but operationally useless because context matters. In high-stakes business environments, close enough isn't good enough. Domain-specific AI agents understand common tasks, such as running time comparisons and generating dashboards, without needing retraining for every task. They know patterns, shared metrics, and filters, and can explain why numbers matter. Examples of this shift are seen in data engineers using AI to validate pipelines and finance teams using AI to reconcile numbers. Building domain-specific AI isn't just about training a model differently; it requires a different architecture. This is achieved with Cube D3, the trustworthy agentic analytics platform, which includes a semantic foundation, governance-first access, explainability and auditability, and interoperability. With these building blocks in place, Cube D3 operates alongside your team, within your rules, and in service of your business goals. Enterprise leaders are growing wary of one-size-fits-all AI and need domain-specific solutions that offer better answers to questions like trustworthiness, integration, ownership, and scalability. Domain-specific AI agents extend people by automating what's tedious, accelerating what's manual, and enhancing what's strategic. The most successful AI deployments in the enterprise won't be general copilots but rather digital teammates that act like domain-specific AI agents.