Researchers propose closed-loop agent evaluation method for deployed AI systems
A new paper on arXiv outlines a framework for continuously evaluating deployed AI agents, not just benchmarking them once. The method, called closed-loop evaluation, models performance under biased feedback and incomplete data. It breaks down analysis into four levels—span, trace, conversation, and engagement—and measures outcomes across four dimensions: Execution, Substance, Trajectory, and…
Key points
- Closed-loop evaluation continuously assesses deployed AI agents, not just via one-off benchmarks
- Four analysis levels (span, trace, conversation, engagement) measure Execution, Substance, Trajectory, and Results
- ICC score of 0.86 for evidence-based scoring vs. 0.81 for holistic methods, per the paper
The approach uses evaluator agents generated by an Agent Factory, each equipped with domain knowledge and adapters. Two feedback loops—an inner loop for auditing evidence and rules, and an outer loop for regression checks—drive iterative improvement. The authors demonstrate the method with a business-analysis agent called SharpInsight and public conversation data, showing higher reliability in evidence-based scoring (ICC 0.86 vs. 0.81) compared to holistic methods.
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