Unverified50% confidenceFactExact time
Expert-in-the-loop in Agent evaluation requires judging whether a series of actions is reasonable; such judgments are hard for non-experts to make, so the decision process of domain experts needs to be structured.
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50%
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Long-term
Relevance
10/31/2026
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VerifiedIn real-world Agent development, evaluating agent performance, debugging erroneous behavior, and setting safety boundaries are often more challenging than building basic functionality.75% similarUnverifiedThe difficulty of evaluating agents lies in the fact that their behavior is sequential and their paths are not unique, requiring comprehensive scoring across dimensions such as task completion rate, action correctness, step efficiency, and safety, often measured using trajectory evaluation and end-to-end task success rate.75% similarUnverifiedAgents in production environments usually need to be paired with constraint, validation, and human fallback mechanisms, because autonomy brings uncertainties such as selecting the wrong tool, loops that fail to converge, and uncontrolled execution costs.74% similarUnverifiedThe judgments of traditional experts can be held accountable, explained, and peer-reviewed, whereas current AI models neither bear professional responsibility nor provide a verifiable chain of reasoning, and their internal processes are highly opaque to users.74% similar
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