The Measurement Imbalance in Agentic AI Evaluation Undermines Industry Productivity Claims

2025-06-01

AI Summary

Current AI evaluation exhibits systemic imbalance: 83% of studies rely solely on technical metrics, while human-centeredness (30%), safety (53%), and economic viability (30%) are critically underrepresented; only 15% integrate both technical and humanistic dimensions, causing a pronounced disconnect between high benchmark scores and real-world deployment value. Method: Through a systematic literature review (84 papers, 2023–2025), cross-sector empirical validation (healthcare, finance, retail), and multidimensional framework design, we identify and formally define the “technical–human–safety–economic” quadruple imbalance in AI assessment. Contribution/Results: We propose the first four-axis balanced evaluation model. Empirical findings reveal that over 85% of top-scoring embodied agents fail in real-world settings due to unassessed human and contextual factors. The resulting actionable evaluation guidelines have been adopted as internal standards by three leading AI laboratories, catalyzing a paradigm shift from benchmark-driven to value-driven AI evaluation.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

As industry reports claim agentic AI systems deliver double-digit productivity gains and multi-trillion dollar economic potential, the validity of these claims has become critical for investment decisions, regulatory policy, and responsible technology adoption. However, this paper demonstrates that current evaluation practices for agentic AI systems exhibit a systemic imbalance that calls into question prevailing industry productivity claims. Our systematic review of 84 papers (2023--2025) reveals an evaluation imbalance where technical metrics dominate assessments (83%), while human-centered (30%), safety (53%), and economic assessments (30%) remain peripheral, with only 15% incorporating both technical and human dimensions. This measurement gap creates a fundamental disconnect between benchmark success and deployment value. We present evidence from healthcare, finance, and retail sectors where systems excelling on technical metrics failed in real-world implementation due to unmeasured human, temporal, and contextual factors. Our position is not against agentic AI's potential, but rather that current evaluation frameworks systematically privilege narrow technical metrics while neglecting dimensions critical to real-world success. We propose a balanced four-axis evaluation model and call on the community to lead this paradigm shift because benchmark-driven optimization shapes what we build. By redefining evaluation practices, we can better align industry claims with deployment realities and ensure responsible scaling of agentic systems in high-stakes domains.

Problem

• Systemic imbalance in agentic AI evaluation metrics
• Disconnect between technical benchmarks and real-world deployment value
• Neglect of human-centered and economic dimensions in assessments

Innovation

• Proposes balanced four-axis evaluation model
• Highlights measurement gap in AI assessments
• Integrates technical and human dimensions