LLM-Powered AI Agent Systems and Their Applications in Industry

TL;DR

This paper reviews the evolution and applications of LLM-powered agent systems across various industries, highlighting their capabilities, challenges, and potential solutions.

Contribution

It provides a comprehensive categorization of LLM-based agent systems, discusses their diverse applications, and addresses key challenges with proposed mitigation strategies.

Findings

LLM-powered agents enable cross-domain reasoning and multimodal data processing.

Applications span customer service, manufacturing, education, finance, and healthcare.

Challenges include latency, uncertainty, evaluation, and security issues.

Abstract

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.