Survey on Evaluation of LLM-based Agents
The emergence of LLM-based agents represents a paradigm shift in AI, enabling
autonomous systems to plan, reason, use tools, and maintain memory while
interacting with dynamic environments. This paper provides the first
comprehensive survey of evaluation methodologies for these increasingly capable
agents. We systematically analyze evaluation benchmarks and frameworks across
four critical dimensions: (1) fundamental agent capabilities, including
planning, tool use, self-reflection, and memory; (2) application-specific
benchmarks for web, software engineering, scientific, and conversational
agents; (3) benchmarks for generalist agents; and (4) frameworks for evaluating
agents. Our analysis reveals emerging trends, including a shift toward more
realistic, challenging evaluations with continuously updated benchmarks. We
also identify critical gaps that future research must address-particularly in
assessing cost-efficiency, safety, and robustness, and in developing
fine-grained, and scalable evaluation methods. This survey maps the rapidly
evolving landscape of agent evaluation, reveals the emerging trends in the
field, identifies current limitations, and proposes directions for future
research.
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Asaf Yehudai
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2025-03-21T11:37:52.838Z
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Asaf Yehudai
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Lilach Eden
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2025-03-21T12:53:28.129Z
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Lilach Eden
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Alan Li
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2025-03-21T11:37:48.565Z
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Alan Li
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Guy Uziel
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Yilun Zhao
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2025-03-21T12:53:35.221Z
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Yilun Zhao
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Roy Bar-Haim
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2025-03-21T12:53:41.335Z
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Roy Bar Haim
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Arman Cohan
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2025-03-21T12:53:51.866Z
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Arman Cohan
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Michal Shmueli-Scheuer
Survey on Evaluation of LLM-based Agents
The emergence of LLM-based agents represents a paradigm shift in AI, enabling
autonomous systems to plan, reason, use tools, and maintain memory while
interacting with dynamic environments. This paper provides the first
comprehensive survey of evaluation methodologies for these increasingly capable
agents. We systematically analyze evaluation benchmarks and frameworks across
four critical dimensions: (1) fundamental agent capabilities, including
planning, tool use, self-reflection, and memory; (2) application-specific
benchmarks for web, software engineering, scientific, and conversational
agents; (3) benchmarks for generalist agents; and (4) frameworks for evaluating
agents. Our analysis reveals emerging trends, including a shift toward more
realistic, challenging evaluations with continuously updated benchmarks. We
also identify critical gaps that future research must address-particularly in
assessing cost-efficiency, safety, and robustness, and in developing
fine-grained, and scalable evaluation methods. This survey maps the rapidly
evolving landscape of agent evaluation, reveals the emerging trends in the
field, identifies current limitations, and proposes directions for future
research.
This survey analyzes evaluation methodologies for large language model-based agents, covering fundamental capabilities, application-specific benchmarks, and generalist agents, highlighting trends and gaps in the field.
LLM-based agents autonomous systems evaluation methodologies planning tool use self-reflection memory web agents software engineering agents scientific agents conversational agents generalist agents evaluation frameworks realistic evaluations continuously updated benchmarks cost-efficiency safety robustness fine-grained evaluation methods scalable evaluation methods