Talk, Evaluate, Diagnose: User-aware Agent Evaluation with Automated Error Analysis
#AI agents #user-aware evaluation #automated error analysis #performance diagnosis #interaction data
📌 Key Takeaways
- The paper introduces a user-aware evaluation framework for AI agents.
- It emphasizes automated error analysis to diagnose agent performance issues.
- The approach integrates user interaction data to improve evaluation accuracy.
- It aims to enhance agent reliability and user experience through systematic assessment.
📖 Full Retelling
arXiv:2603.15483v1 Announce Type: new
Abstract: Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework. Prior works each employ their own methods to determine task success, such as database lookups, regex match, etc., adding complexity to the development of a unified agent evaluation approach. Moreover, they do not systematically account for the
🏷️ Themes
AI Evaluation, Error Analysis
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Original Source
arXiv:2603.15483v1 Announce Type: new
Abstract: Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework. Prior works each employ their own methods to determine task success, such as database lookups, regex match, etc., adding complexity to the development of a unified agent evaluation approach. Moreover, they do not systematically account for the
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