Agent AI: Concepts, Applications, and Future Prospects

Abstract

This paper explores the theoretical underpinnings and practical manifestations of agent-based artificial intelligence, tracing their evolution from early conceptualizations to their contemporary resurgence within advanced AI paradigms. Initially conceived as autonomous programs with persistent states and independent execution threads, agents have evolved significantly, diversifying into various models that offer profound insights into system complexities. This historical trajectory reveals a consistent pattern of agents serving as a crucial bridge between theoretical AI constructs and practical applications, integrating perception, reasoning, and action into cohesive operational structures. The recent integration of large language models has further redefined agent capabilities, thereby enhancing their utility in dynamic and complex environments. This paper examines the methodological advancements, architectural frameworks, and emergent behaviors of AI agents, particularly in the context of their collaborative mechanisms and their potential to drive artificial general intelligence. Specifically, the synergy between traditional agent-based modeling and large language models has yielded a new class of "LLM agents" that exhibit goal-driven behaviors and dynamic adaptation, pushing the boundaries towards artificial general intelligence.