Ontology-driven AI agents utilize formal domain knowledge structures to achieve a clear understanding and reasoning within specific fields. This approach enhances vertical AI agents by providing rich context, logical reasoning, a shared vocabulary, and embedded domain expertise, leading to improved decision quality and data integration. These agents find applications in areas like regulatory compliance, logistics, and maritime operations, with recent case studies highlighting their benefits in urban planning, pharmaceutical supply chains, enterprise automation, and travel. Despite challenges in knowledge acquisition, scalability, and integration, future trends point towards standardized ontologies, deeper integration with large language models, automated ontology learning, and the growth of a specialized AI agent economy, ultimately improving human-AI collaboration.