Agentic frameworks represent a significant leap in artificial intelligence, enabling systems to autonomously perform complex, multi-step tasks by integrating memory, reasoning, and external tool utilization, with large language models like GPT-4 as their cognitive backbone. These frameworks are particularly transformative in dynamic environments such as healthcare, finance, and software development, where they enhance decision-making and automate intricate workflows. Despite their potential, agentic systems face challenges including high complexity, resource demands, and potential overengineering, making them less suitable for simpler tasks where traditional automation or single-agent systems might suffice. Organizations must carefully evaluate their specific needs, task complexity, and available resources to determine whether agentic frameworks offer a strategic advantage or represent an unnecessary investment.