AI Research
Evaluating Tool-Using Language Agents: Judge Reliability, Propagation Cascades, and Runtime Mitigation in AgentProp-Bench
New research exposes the inherent unreliability of automated judging in complex AI agent workflows.
Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, but this assumption has rarely been validated against human annotation. We introduce AgentProp-Bench, a 2,000-task benchmark with 2,300 traces across four domains, nine producti...
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ArXiv CS.AI
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ArXiv CS.AI
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Generated insight by OnAirToday AI
Automated evaluation of AI agents is often dangerously unreliable, with simple judge metrics failing to align with human standards. This research highlights the critical need for robust validation frameworks to prevent silent failure propagation in complex, multi-step agentic workflows.
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