AI Agent可扩展评估：将人类判断前置为可复用资产

>> Scalable Evaluation for AI Agents <<

If you run agent evaluation in production, this one is worth your time.

It shows that front-loading human judgment into reusable evaluation assets is useful.

But why?

Agents reason across turns, call tools, hold context, follow policies, and act under uncertainty, so they have to be judged as behavioral systems.

Current methods each give a fragment. Benchmarks measure fixed capabilities, human review preserves judgment but does not scale, LLM-as-judge inherits the evaluator design problem, red teaming is episodic, and trace audits need explicit evidence rules.

Human-on-the-Bridge puts human expertise upstream, where experts curate reusable evaluation intelligence before testing rather than reviewing each output in the loop.

Paper: https://arxiv.org/abs/2606.16871

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