Auto-Eval Judge: Towards a General Agentic Framework for Task Completion Evaluation

• URL: http://arxiv.org/abs/2508.05508v1
• Date: Thu, 07 Aug 2025 15:39:48 GMT
• Title: Auto-Eval Judge: Towards a General Agentic Framework for Task Completion Evaluation
• Authors: Roshita Bhonsle, Rishav Dutta, Sneha Vavilapalli, Harsh Seth, Abubakarr Jaye, Yapei Chang, Mukund Rungta, Emmanuel Aboah Boateng, Sadid Hasan, Ehi Nosakhare, Soundar Srinivasan,
• Abstract summary: We propose a generalizable, modular framework for evaluating agent task completion independent of the task domain.<n>We validate our framework by evaluating the Magentic-One Actor Agent on two benchmarks, GAIA and BigCodeBench.<n>Our Judge Agent predicts task success with closer agreement to human evaluations, achieving 4.76% and 10.52% higher alignment accuracy, respectively.
• Score: 4.08768677009363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing adoption of foundation models as agents across diverse domains necessitates a robust evaluation framework. Current methods, such as LLM-as-a-Judge, focus only on final outputs, overlooking the step-by-step reasoning that drives agentic decision-making. Meanwhile, existing Agent-as-a-Judge systems, where one agent evaluates another's task completion, are typically designed for narrow, domain-specific settings. To address this gap, we propose a generalizable, modular framework for evaluating agent task completion independent of the task domain. The framework emulates human-like evaluation by decomposing tasks into sub-tasks and validating each step using available information, such as the agent's output and reasoning. Each module contributes to a specific aspect of the evaluation process, and their outputs are aggregated to produce a final verdict on task completion. We validate our framework by evaluating the Magentic-One Actor Agent on two benchmarks, GAIA and BigCodeBench. Our Judge Agent predicts task success with closer agreement to human evaluations, achieving 4.76% and 10.52% higher alignment accuracy, respectively, compared to the GPT-4o based LLM-as-a-Judge baseline. This demonstrates the potential of our proposed general-purpose evaluation framework.

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