Autonomous AI Agents in Smart Ecosystems: A Review of Architectures and Decision-Making Models

Abstract

Autonomous AI agents are increasingly being deployed in smart ecosystems to enable real-time decision-making and adaptive system behavior. This paper presents a comprehensive review of architectures supporting autonomous agents, including multi-agent systems, reinforcement learning frameworks, and hybrid AI models. It explores applications in smart homes, industrial automation, and intelligent transportation systems. Key challenges such as coordination, scalability, and ethical decision-making are analyzed. The study also discusses the integration of large language models with agent-based systems. Future directions include self-learning ecosystems and collaborative human-AI environments.