TASTE automates generation of high-coverage difficult agent benchmarks via adaptive contrastive n-gram sampling of tool sequences, yielding τ^c-Bench where models saturating τ²-Bench drop sharply and unique tool combinations more than double.
Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents
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abstract
Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic evaluation capabilities or imposing static, hand-crafted error taxonomies that cannot adapt to new domains. To address this gap, we present Agentic CLEAR, an automatic, dynamic, and easy-to-use evaluation framework. It produces textual insights into the agent behavior on three levels of granularity: system, trace, and node. Agentic CLEAR operates above the observability layer, enabling seamless integration and featuring an intuitive UI that makes agent evaluation highly accessible. In our experiments on four benchmarks, seven agentic settings, and tens of thousands of LLM calls, we show that Agentic CLEAR produces high-quality, data-driven, insightful feedback. Our analysis shows strong alignment with human-annotated errors and the ability to predict task success rate.
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks
TASTE automates generation of high-coverage difficult agent benchmarks via adaptive contrastive n-gram sampling of tool sequences, yielding τ^c-Bench where models saturating τ²-Bench drop sharply and unique tool combinations more than double.