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This paper introduces the Insights Generator, a multi-agent system for systematic corpus-level trace diagnostics of LLM agents, which generates evidence-backed insights by proposing and testing hypotheses across execution traces. Experiments show that using Insights Generator reports improves scaffold performance by 30.4 percentage points.