Multi-Component AI Architectures with Meta-Learning

Abstract

ABSTRACT Artificial Intelligence (AI) systems have evolved from single-model pipelines to complex multi-component architectures capable of solving diverse and dynamic problems. However, as these systems grow in scale and heterogeneity, issues such as coordination, adaptability, and knowledge transfer become more pronounced. Multi-component AI architectures integrate different models, agents, or modules that cooperate to achieve shared objectives. When combined with meta-learning strategies, such architectures gain the ability to adapt rapidly to new tasks with minimal data. This paper reviews the conceptual foundations, architectural patterns, learning mechanisms, and application domains of multi-component AI systems enhanced by meta-learning. We analyze different architectural paradigms including ensemble systems, modular networks, hierarchical agents, and hybrid neuro-symbolic frameworks. Furthermore, we discuss meta-learning techniques such as model-agnostic meta-learning, metric-based learning, and memory-augmented approaches that enable faster convergence and improved generalization. Several practical case studies are examined across healthcare, robotics, and natural language processing. Challenges such as scalability, interpretability, and resource management are also highlighted. The paper concludes by outlining emerging research directions, including autonomous architecture search and continual meta-adaptation.

KEYWORDS: Multi-component AI, Meta-learning, Modular neural networks, Adaptive systems, Ensemble learning, Transfer learning, Autonomous agents, Continual learning