The research paper Survey on Evaluation of LLM-based Agents provides the first comprehensive map of how to measure the performance and trustworthiness of AI agents that can reason, plan, and act autonomously. Unlike traditional models that simply generate text, these new agentic systems interact with tools, data, and people, making old benchmarks obsolete. The authors review more than 100 evaluation frameworks and propose a new taxonomy that accounts for reasoning depth, adaptability, tool use, and safety. For business leaders, the relevance is clear: as AI agents increasingly take on decision-making and operational roles, the ability to measure performance, reliability, and compliance becomes a critical part of enterprise AI governance. This paper effectively establishes the foundation for building and certifying trustworthy AI agents in commercial, scientific, and policy contexts.

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Key point: This paper establishes a comprehensive framework for evaluating large language model-based agents, highlighting that new metrics for reasoning, safety, and adaptability are essential to ensure trustworthy and effective AI systems in real-world applications.