Recent research in the field of Large Language Models (LLMs) has given a new direction to the capabilities of AI agents for solving complex problems. This paper attempts to explore one such use case to investigate LLMs-based AI agents' role in immersive technology, specifically focusing on GPT-4's vision capabilities in Augmented Reality (AR). The paper utilizes Smart App Agent Framework for recommending products. This recommendation system assists users to make context-aware decisions during their online shopping experience.

This paper explores the significant shift towards agentic workflows in the application of Large Language Models (LLMs), moving away from traditional, linear interactions between users and AI. Through a case study analysis, we highlight the effectiveness of agentic workflows, which facilitate a more dynamic and iterative engagement, in improving outcomes in tasks such as question answering, code generation or stock analysis. Central to the agentic workflow are four foundational design patterns: reflection, planning, multi-agent collaboration, and tool utilization. These components are crucial for boosting LLM productivity and enhancing performance. The study demonstrates how agentic workflows, by promoting an iterative and reflective process, can serve as a crucial step towards achieving Artificial General Intelligence (AGI).