AI has rapidly evolved from generating text to developing full-fledged agents capable of reasoning, tool orchestration, and completing complex tasks autonomously, offering transformative potential for enterprises. However, to leverage these capabilities effectively, organizations must adapt their evaluation practices to account for agents' unique characteristics, such as their reasoning abilities and non-deterministic outputs, which differ from traditional software. Unlike deterministic applications, agents can reason on-the-fly, adapt, and take multiple valid paths, necessitating new evaluation methods that consider task success, business value, reasoning effectiveness, trust, and operational performance. Enterprises must focus on iterative evaluation, contextual benchmarks, and cross-functional governance to ensure agents deliver reliable, safe, and scalable business outcomes. As agents pose systemic risks if not properly evaluated, early adoption of rigorous evaluation frameworks will provide competitive advantages, enabling organizations to transition from pilot projects to full-scale production and realize AI's transformative potential.