LLM-Powered AI Agent Systems and Their Applications in Industry

2025-05-22

AI Summary

This paper addresses core challenges hindering industrial deployment of LLM-based agents—namely, high inference latency, output non-determinism, lack of standardized evaluation, and safety risks. Methodologically, it proposes the first industrial-oriented LLM agent system framework: (1) it introduces a systematic taxonomy of agent architectures—software-only, physically embodied, and adaptive hybrid—tailored to real-world industrial constraints; and (2) it establishes a full-stack multimodal (text/image/audio/tabular) pipeline encompassing perception, memory, planning, tool orchestration, and safety-enhanced reasoning. Contributions include deployable prototypes across customer service, manufacturing, education, finance, and healthcare; significant reduction in task-specific customization effort; improved cross-domain generalization and human-agent interaction fluency; and, critically, a reusable, industry-grade evaluation benchmark and safety governance paradigm for production LLM agents.

Technology Category

Cognitive Modeling & Cognitive Systems: Agent ArchitecturesMachine Learning: Large Multimodal Models (LMMs)Multiagent Systems: Agent/AI Theories and Architectures

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systemsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Exploring LLM-powered agent systems' evolution and capabilities
• Analyzing applications of AI agents across diverse industries
• Addressing challenges like latency and security in LLM agents

Innovation

• LLM-powered agents enable cross-domain reasoning
• Multi-modal LLMs process diverse data types
• Hybrid systems integrate software and physical agents