Microsoft's Satya Nadella has shifted his focus to "agents as a service," indicating a significant shift in how AI works across industries. Agentic systems, which combine large language model calls, database lookups, and more, are making waves with their ability to function independently and tackle complex tasks. These systems have a cognitive architecture that enables them to break down complex objectives into manageable sub-tasks, much like a human professional would approach a multifaceted project. Agentic systems rely on prompt engineering that guides the decision-making process, memory components enable agents to recall past interactions and maintain context throughout complex conversations, and API integrations serve as the agent's connection to the external world. The design and purpose differences between traditional AI and agentic systems are stark, with agentic systems being built to mimic human decision-making across multiple tasks. Autonomy represents a significant distinction, allowing these systems to determine the next steps independently based on context and goals. Task completion metrics reveal alignment between user intent and agent action, highlighting instances where agents misinterpret requests. The granularity of these metrics matters significantly, as they should measure both overall task success and the accuracy of individual steps within multi-stage processes. Customer satisfaction correlates strongly with successful task completion, making these metrics valuable business indicators beyond technical performance. Agentic systems show tremendous potential in industries where automation and efficiency matter, such as financial services, where digital agents can function as financial advisors, analyzing complex data for personalized guidance. As AI agents become increasingly integrated into business operations, performance evaluation becomes critical for reliability and effectiveness.