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LLM agents automating productivity tasks achieve only moderate success (39-64%) while exhibiting surprisingly high rates of unsafe actions (7-33%) in realistic, multi-service workflows.
Scaling prompt learning by 17x without sacrificing accuracy is now possible, unlocking efficient self-improvement for LLM agents.
