Mehta 2025 argues that current agentic AI benchmarks predominantly measure task-completion accuracy while overlooking the requirements that matter for enterprise deployment, such as cost-efficiency, reliability, and operational stability.
The cited abstract states verbatim that current agentic AI benchmarks predominantly evaluate task completion accuracy while overlooking critical enterprise requirements such as cost-efficiency, reliability, and operational stability.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 04:35
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“Current agentic AI benchmarks predominantly evaluate task completion accuracy, while overlooking critical enterprise requirements such as cost-efficiency, reliability, and operational stability.”
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Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems Sushant Mehta sushant0523@gmail.com Abstract 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 costcontrolled evaluation leading to 50x cost variations for similar precision, (2) inadequate reliability assessment where agent performance drops from 60% (single run) to 25% (8run consistency), and (3) missing multidimensional metrics for security, latency, and policy compliance. We propose 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.
Passage read from arxiv.org, which may be a preprint rather than the published version.