Towards Outcome-Oriented, Task-Agnostic Evaluation of AI Agents

The proliferation of AI agents across various industries has raised concerns about the adequacy of traditional evaluation metrics, which often focus on infrastructural aspects like latency and throughput. A recent white paper addresses this gap by proposing a novel framework consisting of eleven outcome-based, task-agnostic performance metrics. These metrics, including Goal Completion Rate (GCR) and Business Impact Efficiency (BIE), are designed to evaluate AI agents on their decision quality, operational autonomy, and adaptability to new challenges. The framework was tested through a large-scale simulated experiment involving four distinct agent architectures—ReAct, Chain-of-Thought, Tool-Augmented, and Hybrid—across five domains: healthcare, finance, marketing, legal, and customer service. The findings indicate that the Hybrid Agent consistently outperformed others across most proposed metrics, underscoring the need for a shift in how organizations assess AI performance to ensure the…