LLM-Powered AI Agent Systems and Their Applications in Industry
It addresses the problem of integrating LLMs into agent systems for various industries, but it is incremental as it reviews existing developments rather than introducing new methods.
This paper examines the evolution of agent systems from pre-LLM to LLM-powered architectures, highlighting their applications in industries like customer service and healthcare, and discusses challenges such as high inference latency and security vulnerabilities.
The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility, cross-domain reasoning, and natural language interaction. Moreover, with the integration of multi-modal LLMs, current agent systems are highly capable of processing diverse data modalities, including text, images, audio, and structured tabular data, enabling richer and more adaptive real-world behavior. This paper comprehensively examines the evolution of agent systems from the pre-LLM era to current LLM-powered architectures. We categorize agent systems into software-based, physical, and adaptive hybrid systems, highlighting applications across customer service, software development, manufacturing automation, personalized education, financial trading, and healthcare. We further discuss the primary challenges posed by LLM-powered agents, including high inference latency, output uncertainty, lack of evaluation metrics, and security vulnerabilities, and propose potential solutions to mitigate these concerns.