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

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