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• Current AI agent evaluation practices are fragmented and measure isolated capabilities in narrowly defined settings.limitation 95%
• The proposed Holographic Agent Assessment Framework (HAAF) evaluates agent trustworthiness over a representative socio-technical scenario distribution.method 95%
• HAAF integrates four complementary components: static cognitive and policy analysis, interactive sandbox simulation, social-ethical alignment assessment, and a distribution-aware representative sampling engine.method 95%
• HAAF's sampling engine optimizes for coverage and risk sensitivity, including rare but high-consequence tail risks.method 95%
• The HAAF paradigm uses iterative red-team probing and blue-team hardening to narrow vulnerabilities.method 95%
• The market lacks integrated evaluation tools for AI agents, creating a gap for solutions that prevent costly mistakes.market 90%
• Implementing HAAF may face high implementation complexity requiring domain expertise and potential high computational costs for comprehensive simulations.limitation 85%
• The proposed framework aims to shift agent evaluation from 'benchmark islands' toward representative, real-world trustworthiness.method 95%

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