The field of artificial intelligence is witnessing significant developments in AI-augmented learning and multi-agent systems. Researchers are exploring innovative architectures and design patterns to support personalized and scalable education, as well as autonomous and interactive agents that can perceive, reason, and act without human intervention. A key trend is the shift towards microservices architecture in multi-agent systems, which improves scalability and maintainability. Another area of focus is the development of unified, agent-centric infrastructures that enable seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents. Notable papers in this area include:

• A4L: An Architecture for AI-Augmented Learning, which presents a data architecture for collecting and analyzing data on learning and feeding the results back to teachers, learners, and AI agents.
• AgentFlow: Resilient Adaptive Cloud-Edge Framework for Multi-Agent Coordination, which introduces a framework for programmable distributed systems in heterogeneous cloud-edge environments.
• Agent-as-a-Service based on Agent Network, which proposes a service-oriented paradigm for organizing agent-level collaboration and unifying the entire agent lifecycle.