Abstract: Knowledge tracing, as the core task of Intelligent Tutoring Systems, aims to predict students' future learning performance by analyzing their historical behavioral data, and provides a fundamental basis for personalized learning path planning. Current mainstream deep learning methods are constrained by knowledge boundaries and rely on single-feature modeling, often exhibiting issues such as overly simplified learning process modeling, inadequate contextual adaptability, and black-box prediction mechanisms that yield results lacking interpretability and difficult to translate into actionable teaching references. To address these challenges, this paper proposes CAKT-Agent, a context-adaptive knowledge tracing agent based on Mixture-of-Experts model, and constructs a three-tier collaborative architecture consisting of a unified memory module, a learning agent, and a companion agent: the unified memory module realizes global storage and sharing of learning data, educational priors and intermediate model states; The learning agent defines a five-dimensional context based on educational measurement theory and cognitive science, designs a hybrid expert architecture with dimension-aware capabilities, and achieves context-adaptive integration through a two-level gating mechanism; the companion agent generates learning diagnostic reports with educational comprehensibility based on expert activation weights and educational theory rules. Experimental results on real-world datasets show that the proposed model outperforms mainstream KT models in the prediction accuracy of students' learning performance, with excellent context-adaptive capability and result comprehensibility.