Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems
This addresses the need for more comprehensive evaluation metrics in enterprise AI deployments, though it is incremental as it builds on existing benchmarking approaches.
The paper tackles the problem of evaluating agentic AI systems for enterprises by identifying limitations in current benchmarks that focus only on accuracy, and proposes the CLEAR framework to incorporate cost, reliability, and other factors, showing that accuracy-optimized agents can be 4.4-10.8x more expensive than cost-aware alternatives and that CLEAR better predicts production success with a correlation of 0.83.
Current agentic AI benchmarks predominantly evaluate task completion accuracy, while overlooking critical enterprise requirements such as cost-efficiency, reliability, and operational stability. Through systematic analysis of 12 main benchmarks and empirical evaluation of state-of-the-art agents, we identify three fundamental limitations: (1) absence of cost-controlled evaluation leading to 50x cost variations for similar precision, (2) inadequate reliability assessment where agent performance drops from 60\% (single run) to 25\% (8-run consistency), and (3) missing multidimensional metrics for security, latency, and policy compliance. We propose \textbf{CLEAR} (Cost, Latency, Efficacy, Assurance, Reliability), a holistic evaluation framework specifically designed for enterprise deployment. Evaluation of six leading agents on 300 enterprise tasks demonstrates that optimizing for accuracy alone yields agents 4.4-10.8x more expensive than cost-aware alternatives with comparable performance. Expert evaluation (N=15) confirms that CLEAR better predicts production success (correlation $ρ=0.83$) compared to accuracy-only evaluation ($ρ=0.41$).