Artificial intelligence (AI) agents are gaining importance in educational technology, helping in terms of personalization and engagement but gaps remain in how these systems are integrated at scale. This paper synthesizes recent empirical studies and field reports investigating the efficacy, obstacles, and opportunities of AI agents in education and professional training. Drawing on that synthesis, we propose a five-layer architecture that tackles the main integration challenges. The architecture design process involved analyzing the functional requirements and interaction patterns identified across the three educational domains, then designing a modular system that enables composability of specialized agents, service reuse through common AI engines, and comprehensive auditability through centralized data management. The architectural validation employs conceptual demonstration through two representative interaction flows that operate across different temporal scales, proving the framework’s ability to support both real-time micro-interventions and long-term analytical insights without code duplication. The integration of large language models that can handle open-ended conversations and provide deeper analyses of student responses offers significant potential for advancing the sophistication of AI tutoring systems.