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This paper introduces Who&When Pro, a large-scale benchmark for automated failure attribution in AI agentic systems, with 12,326 failure trajectories across 26 benchmarks, and provides insights into how LLMs attribute failures.
This post explains how to create an automated feedback loop for AI agents to iteratively improve their skills, using computer use and an observer skill to evaluate and update the skill code.