AI agents have evolved from simple automation tools to sophisticated digital colleagues that plan, adapt, and improve over time. However, measuring their performance poses unique challenges due to their complex behavior, variable performance degradation, and multi-dimensional success criteria. Organizations need a structured approach to ensure their AI agents maintain and deliver measurable business value by implementing key metrics such as LLM Call Error Rate, Task Completion Rate, Number of Human Requests, Token Usage per Interaction, Tool Success Rate, Context Window Utilization, Steps per Task, Total Task Completion Time, Output Format Success Rate, and Cost per Task Completion. By optimizing these metrics, organizations can identify areas for improvement, understand bottlenecks, and justify continued AI investments. Effective measurement and optimization of AI agent performance are crucial to unlock their full potential and create new possibilities for innovation.