AI Agents for Business Applications: A Review

Abstract

Artificial Intelligence (AI) agents have revolutionized business applications by automating processes, enhancing decision-making, and optimizing operational efficiency. This paper presents a comprehensive review of AI agents, categorizing their applications across domains such as customer relationship management (CRM), supply chain management, financial forecasting, and enterprise decision support systems. The evolution of AI agents from rule-based models to sophisticated multi-agent systems (MAS) and large language models (LLMs) has enabled businesses to leverage intelligent automation, real-time analytics, and predictive insights. AI-driven conversational agents have improved customer engagement, while AI-powered workflow automation has enhanced IT operations and security. Despite these advancements, challenges such as ethical considerations, security risks, interoperability, and long-term adaptability persist. This review synthesizes research contributions, identifying key strengths, limitations, and emerging research gaps in AI adoption for business. Future directions highlight the need for enhanced human-AI collaboration, standardization of AI agent interoperability, security-first AI architectures, and emotionally intelligent conversational systems. Addressing these challenges will ensure the responsible and effective deployment of AI agents, maximizing their transformative potential in business environments.