The scientific and clinical community relies on the active development of a wide range of interlinked knowledge bases in order to plan experiments, interpret omics data, and to help with the diagnosis and treatment of disease. These knowledge bases make use of expert curation and the use of community ontologies in order to provide accurate and structured information that can be used algorithmically. The advent of generative AI and agentic methods presents fantastic opportunities for accelerating curation, increasing the breadth and depth of coverage. Open knowledge bases also present opportunities to generative AI, in the form of a trusted backbone of knowledge that can mitigate the hallucinations that plague large language models. However, the pace of development of AI, combined with misunderstandings about both strengths and weaknesses, poses significant dangers. In this talk, I will present our recent work on the use of agentic AI to assist with manual knowledge base tasks, particularly those involving complex ontology development and maintenance tasks. I will present a realistic picture of challenges we face, but also strategies to mitigate them, and a path towards a future where agents, curators, and others can work together to leverage and integrate open source tools and data along with the combined knowledge of the scientific community.NOTE: this is an abridged version, the full version is at: https://f1000research.com/slides/14-735