LLM-Powered AI Agent Systems and Their Applications in Industry
Guannan Liang, Qianqian Tong
Why It Matters
What makes this one worth your time
Understanding the integration of LLMs into agent systems is crucial for developing more flexible and capable AI applications across industries.
The paper examines the evolution and industrial applications of LLM-powered agent systems, highlighting challenges and solutions.
Summary
The paper explores the transformation of agent systems with the advent of Large Language Models (LLMs), categorizing them into software-based, physical, and adaptive hybrid systems, and discusses their applications in various industries. It also addresses challenges such as high inference latency, output uncertainty, lack of evaluation metrics, and security vulnerabilities, proposing potential solutions.
Key contributions
- Comprehensive examination of the evolution of agent systems with LLMs.
- Categorization of agent systems into three types: software-based, physical, and adaptive hybrid.
- Discussion of challenges and proposed solutions for LLM-powered agents.
Notable insights
- The integration of multi-modal LLMs allows agent systems to process diverse data modalities, enhancing adaptability.
- Categorizing agent systems into software-based, physical, and adaptive hybrid systems provides a structured approach to understanding their applications.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2505.16120v3 Announce Type: replace 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.