AI agents are revolutionizing software development by automating workflows and handling tasks independently, boosting efficiency and shaping how new products are built. They are driven by generative AI and various AI agent frameworks, paving the way for rapid innovation. AI agents have already started to reshape industries such as finance, telecommunications, and regulatory defense with unprecedented efficiency, taking over routine and labor-intensive tasks like processing transactions, customer service management, and document reviews. By freeing up human workforce to focus on more creative and strategic work, AI agents bring numerous benefits including higher productivity, scaling without needing proportional staff increase, and enabling growth through generative AI adoption. However, ensuring their reliability requires advanced evaluation tools, which are critical to the process. Understanding the right metrics for evaluating AI agents is also crucial as they take on complex tasks beyond just generating text. The challenge lies in moving beyond simple observations and utilizing advanced LLM evaluation techniques to assess their effectiveness. With proper evaluation, AI agents can prevent costly errors before they happen, ensuring security, compliance, and performance in AI-driven futures.