AI AGENTS: A SYSTEMATIC REVIEW OF ARCHITECTURES, COMPONENTS, AND EVOLUTIONARY TRAJECTORIES IN AUTONOMOUS DIGITAL SYSTEMS
Keywords:
Artificial Intelligence Agents, Autonomous Systems Architecture, Large Language Models, Reinforcement Learning Systems, Multi-agent CollaborationAbstract
This comprehensive article examines artificial intelligence agents' evolution and current state, analyzing their progression from simple reactive systems to sophisticated utility-based architectures. The article systematically analyzes modern AI agent frameworks, exploring the fundamental components that enable autonomous decision-making, including Large Language Models, Reinforcement Learning mechanisms, and Knowledge Graph implementations. The article investigates recent innovations in agent reasoning capabilities, particularly focusing on chain-of-thought prompting, self-reflection mechanisms, and advanced task-planning frameworks. The article explores how these technologies integrate to enable complex decision-making processes and autonomous behavior in varied environments. The article encompasses emerging trends in multi-agent collaboration systems and retrieval-augmented generation techniques, highlighting their impact on agent performance and capability enhancement. The article review concludes by addressing critical challenges in scalability, integration, and ethical considerations while outlining promising directions for future research in autonomous agent development. This article contributes to the growing knowledge of AI agent architectures and provides insights into their potential future trajectories in both theoretical advancement and practical applications.
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Copyright (c) 2025 Mohit Bharti (Author)
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