From Generative to Agentic: How AI Shapes the Future of Work

Abstract

Artificial intelligence has developed from basic automation into more complex systems which demonstrate creativity and autonomous capabilities in recent years. The three most popular AI developments include Generative AI, AI Agents, and Agentic AI, which represent distinct levels of intelligence, autonomy, and functionality. The terms Generative AI, AI Agents, and Agentic AI represent separate concepts within AI despite being classified under the same category (Sakhare et al., 2025; Sapkota et al., 2025). Users and researchers, together with professionals and decision-makers, need to understand these distinctions because they determine the appropriate AI capabilities for their specific requirements. The three AI models serve distinct functions in industries through their ability to generate text, automate tasks, and achieve complex goals independently. The analysis includes simple definitions and explanations, together with comparative applications to differentiate between the three fast-emerging AI models.

The transition from traditional AI systems to modern agentic frameworks represents a major transformation in computer thinking capabilities. The development of multi-agent systems and expert systems established fundamental principles which led to new concepts about distributed intelligence and social action between independent agents. These initial systems operated mainly through predefined rules and symbolic reasoning methods. Modern AI systems lack the learning-driven and context-aware features that these early systems did not possess. Large language models introduced significant changes to this field. Modern systems have progressed beyond basic reactions because they now possess advanced reasoning and planning capabilities. The technological evolution resulted in multiple AI systems with varying degrees of autonomy and decision-making capabilities. The implementation of these systems requires careful consideration regarding their appropriate deployment locations and methods (Sapkota et al., 2025).