A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond
🤖 AI Summary
Current AI agents face significant challenges in unifying cognitive modeling, planning, and interactive behavior, as well as ensuring reliable deployment. This paper proposes a unified agent framework that integrates principles from cognitive science, hierarchical reinforcement learning (HRL), and large language model (LLM)-based reasoning to systematically unify perception, decision-making, and interaction. Methodologically, it employs interdisciplinary collaborative modeling, incorporates explainability mechanisms and formal safety constraints, and synergizes multi-agent coordination with deep reinforcement learning to enhance robustness and adaptivity in dynamic, complex environments. The core contributions are threefold: (1) the first end-to-end theoretical pathway bridging cognitive modeling to trustworthy deployment; (2) identification of key technical breakthrough directions; and (3) an architecture blueprint and practical implementation guidelines for next-generation trustworthy, adaptive intelligent systems—balancing theoretical rigor with engineering feasibility.
Application Category
📝 Abstract
Artificial Intelligence (AI) agents have rapidly evolved from specialized, rule-based programs to versatile, learning-driven autonomous systems capable of perception, reasoning, and action in complex environments. The explosion of data, advances in deep learning, reinforcement learning, and multi-agent coordination have accelerated this transformation. Yet, designing and deploying unified AI agents that seamlessly integrate cognition, planning, and interaction remains a grand challenge. In this review, we systematically examine the architectural principles, foundational components, and emergent paradigms that define the landscape of contemporary AI agents. We synthesize insights from cognitive science-inspired models, hierarchical reinforcement learning frameworks, and large language model-based reasoning. Moreover, we discuss the pressing ethical, safety, and interpretability concerns associated with deploying these agents in real-world scenarios. By highlighting major breakthroughs, persistent challenges, and promising research directions, this review aims to guide the next generation of AI agent systems toward more robust, adaptable, and trustworthy autonomous intelligence.
Problem
Research questions and friction points this paper is trying to address.
Designing unified AI agents integrating cognition, planning, and interaction
Addressing ethical, safety, and interpretability concerns in AI deployment
Advancing robust, adaptable, and trustworthy autonomous intelligence systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep learning and reinforcement learning advancements
Multi-agent coordination for complex environments
Large language model-based reasoning integration
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