AGENTIC AI MODELING FRAMEWORK WITH TECHNICAL ANALYSIS AND APPLICATIONS

Abstract

Agentic AI models enable autonomous decision-making and self-directed learning, distinguishing them from traditional AI. This paper provides an analysis of these models, focusing on their architectures, fine-tuning methods, and future impact.

The study covers the evolution from rule-based systems to advanced transformer-based models like GPT-4 and multimodal models such as Gemini. It categorizes AI models into text-based, image/video generation, speech, code generation, 3D modeling, and multimodal AI, analyzing their strengths, weaknesses, and applications in various fields. Key AI architectures, including transformers, CNNs, RNNs, diffusion models, and reinforcement learning frameworks, are explored. The paper compares their trade-offs in terms of efficiency, scalability, and task-specific performance.

The paper reviews fine-tuning techniques like RLHF, PEFT, and continual learning, and discusses their importance in optimizing AI for specialized tasks. It also addresses evaluation, deployment, and monitoring to ensure AI reliability in real-world applications. Despite advances, challenges remain in computational efficiency, ethical alignment, and interpretability. The paper concludes by emphasizing the need for responsible AI development and improved resilience to adversarial threats. As agentic AI evolves, its integration into industries will transform human-machine collaboration