arXiv:2602.06486v2 Announce Type: replace Abstract: Evaluating agentic AI on open-ended professional tasks faces a fundamental dilemma between rigor and flexibility. Static rubrics provide rigorous, reproducible assessment but fail to accommodate diverse valid response strategies, while LLM-as-a-judge approaches adapt to individual responses yet suffer from instability and bias. Human experts address this dilemma by combining domain-grounded principles with dynamic, claim-level assessment. Inspired by this process, we propose \textbf{JADE}, a two-layer evaluation framework. Layer 1 encodes exp
The rapid advancement of large language models and autonomous agents necessitates more robust and dynamic evaluation methodologies to ensure their reliability and safety in complex tasks.
Improved evaluation frameworks like JADE are crucial for accelerating the deployment and trustworthiness of AI agents in professional settings, directly addressing current limitations in assessment.
The ability to more comprehensively and rigorously evaluate AI agent performance on open-ended professional tasks, moving beyond static rubrics or unstable LLM-as-a-judge approaches, changes how agents will be developed and benchmarked.
- · AI Agent developers
- · Businesses adopting AI agents
- · AI evaluation platforms
- · Traditional static AI evaluation methods
- · Unreliable LLM-as-a-judge systems
More reliable and capable AI agents will be deployed in complex professional workflows.
Increased adoption of AI agents could lead to significant productivity gains and workflow automation in various professional sectors.
Standardization of evaluation methods could foster greater trust and accelerate the societal integration of autonomous AI systems.
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Read at arXiv cs.AI