Exploring Generative AI Agents: Architecture, Applications, and Challenges

Abstract

Abstract The development of Generative AI agents creates a major advancement in AI technology that unites large language model generation power with self-guided decision processes and operation frameworks. Generative agents differ from conventional AI systems because they were made to do sequential reasoning while learning to modify between environments and join with external tools and APIs to handle complex duties. These agents generate complete content alongside plans and computer code and dialogues as well as structured operational elements in different application systems. This paper establishes a complete technical analysis of generative AI agents by explaining their structural frameworks, deployment methods, and implementation approaches. Infrastructures of these systems progress from base language models through present-day agents AutoGPT and BabyAGI as well as LangChain-based agents which serve as the main focus of this article. The paper performs an analysis of vital design elements that comprise memory systems together with planning mechanisms and, tool connectivity features and feedback control structures and protocols for agent-environment communication. This article provides substantial architectural insights while reviewing real-world utilizations in addition to detailing the major obstacles that affect generative AI agent scalability and reliability. The analyzed applications originate from healthcare settings, scientific research environments, the business automation industry, and the creative industries domain. This article reviews the technical and ethical problems, which include hallucination control autonomous error correction interpretability, and data protection systems. This research synthesizes existing studies and implementation practices to deliver critical details about responsible frontier development of generative AI agents for developers as well as researchers and policymakers.

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