Evaluation and Benchmarking of LLM Agents: A Survey

What is it about?

AI agents powered by large language models (LLMs)—like those used in chatbots or automated assistants—are becoming common in everyday tools and business workflows. But testing how well they actually perform remains unclear. Our survey offers a practical guide for evaluating these agents: what to measure (like accuracy, reliability, and safety) and how to measure it (with tools, data, and methods). We also highlight real-world challenges like privacy rules and the need for consistent behavior over time—making this work especially relevant for companies deploying AI systems in practice.

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Why is it important?

As AI agents powered by large language models (LLMs) are increasingly used in real-world applications, the ability to evaluate them effectively has become a pressing challenge. In working with these systems, we found that evaluation goes far beyond simple accuracy—it involves understanding how agents behave over time, how they interact with tools, and whether they comply with enterprise needs like data access and security. Yet the landscape of research and practice is full of different tools, benchmarks, and evaluation methods. This paper offers a framework to organize that complexity. By bringing structure and clarity to a rapidly evolving field, this work supports both researchers and practitioners in building more trustworthy, safe, and scalable agentic AI systems.

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