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This Apple research paper introduces 'Reinforced Agent,' a method that moves evaluation into the execution loop using a specialized reviewer agent to correct tool-calling errors in real-time. It demonstrates significant accuracy improvements on benchmarks like BFCL and τ²-Bench without retraining the base agent.