Beyond Accuracy: A Multi-faceted Evaluation Framework for Real-World AI Agents

AI agents are increasingly deployed in real-world applications, from autonomous systems to conversational assistants. Existing evaluation benchmarks predominantly emphasize task-specific metrics such as accuracy, reward maximization, or F1 scores. However, these metrics often overlook critical dimensions like cost-effectiveness, robustness to dynamic environments, and long-term adaptability. This paper critically analyzes the limitations of current evaluation approaches and proposes a comprehensive multi-faceted framework that integrates these dimensions. We outline key evaluation criteria, present case studies, and suggest a dynamic benchmarking protocol to bridge the gap between controlled experimental setups and real-world deployment requirements. Our framework is designed to enhance the reliability, fairness, and sustainability of AI systems in diverse applications.