Online education has become a new popular educational mode, but its quality issues are also becoming increasingly evident, so effective diagnosis and personalized interventions for online learning are crucial. This paper first reviews and analyzes the current research status and development trends from five perspectives: learning status perception and recording, learning risk cognitive diagnosis, learning crisis grading and warning, learning improvement interventions, and learning early warning information technology tools. Most current early warning methods remain at the measurement level and have not delved into the cognitive level, and the results obtained often focus on presentation rather than prediction, which makes it challenging to implement personalized interventions. Then, on the basis of modern cognitive diagnosis theory and generative AI technology, this paper proposes the concept of an artificial intelligence agent for learning early warnings in online education, which can integrate the efficiency of data-driven models, the interpretability of knowledge-driven models, and the scale benefits of new technologies. The findings of this research can provide insights into the current shortcomings of learning early warning methods and propose an innovative early warning AI agent tool that integrates knowledge, behavior, and emotional monitoring with academic improvement, which is valuable for improving online learning quality and enhancing the effectiveness and efficiency of early warning management.