Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents

• URL: http://arxiv.org/abs/2510.02837v1
• Date: Fri, 03 Oct 2025 09:19:15 GMT
• Title: Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
• Authors: Wonjoong Kim, Sangwu Park, Yeonjun In, Sein Kim, Dongha Lee, Chanyoung Park,
• Abstract summary: A proper evaluation of an agent's performance must go beyond the final answer to also assess the problem-solving trajectory.<n>We introduce TRACE, a framework for the multi-dimensional evaluation of tool-augmented LLM agent performance.<n>Our results confirm that TRACE accurately evaluates these complex behaviors in a scalable and cost-effective manner.
• Score: 22.781523439717223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although recent tool-augmented benchmarks incorporate complex user requests and diverse tools, the evaluation methods for most of them remain limited to answer matching. However, as the number of steps required to resolve a user request increases, a proper evaluation of an agent's performance must go beyond the final answer to also assess the problem-solving trajectory, including previously ignored aspects such as efficiency, hallucination, and adaptivity. The most straightforward method for evaluating these aspects is to compare an agent's trajectory with the ground-truth trajectory, but this approach is fundamentally limited since annotating all valid ground-truth trajectories is prohibitively expensive. However, a simple LLM-based evaluator struggles to assess trajectories in detail without ground truth. To effectively evaluate the agents in this manner, we introduce TRACE, a framework for the multi-dimensional evaluation of tool-augmented LLM agent performance. By incorporating an evidence bank, which accumulates knowledge gathered from preceding reasoning steps, TRACE enables a multi-faceted analysis and evaluation of an agent's reasoning trajectory effectively. To validate our framework, we develop a new meta-evaluation dataset by augmenting existing benchmarks with diverse and flawed trajectories, each labeled with multi-faceted performance scores. Our results confirm that TRACE accurately evaluates these complex behaviors in a scalable and cost-effective manner, even with small open-source LLMs. Furthermore, we apply our method to evaluate the trajectories that agents produce while solving tool-augmented tasks, presenting previously unreported observations and their corresponding insights.

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