Domain-specific agents emerge from the deep integration of large language models (LLMs) with domain knowledge, data systems, and business tools. They are evolving from auxiliary applications for knowledge question answering into execution systems for complex tasks. As sources of domain data diversify, business rules grow more complex, and external tools proliferate, LLMs alone struggle to maintain semantic consistency across task understanding, resource invocation, and result assessment. Ontology enhancement formally represents domain objects and their relations and incorporates business conditions, operational capabilities, and runtime states into a unified semantic framework. It thus provides domain-specific agents with a stable knowledge foundation and a verifiable basis for task execution. This study first defines the concept and scope of ontology-enhanced domain-specific agents, develops an ontology semantic model and a layered technical framework, and reviews key technologies for ontology construction, knowledge acquisition, tool invocation, task planning, and result verification. It then conducts an application validation from a large manufacturing enterprise. The findings indicate that ontology enhancement can reduce semantic deviations in multi-constraint queries by explicitly representing business objects, relation paths, metric definitions, temporal conditions, and state rules, thereby providing verifiable semantic support for domain-specific agents accessing heterogeneous business data.