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This paper introduces Agri-SAGE, a closed-loop framework that integrates multi-agent LLM reasoning with biophysical simulation (APSIM) to generate and validate context-aware agricultural advisories. The framework outperforms static baselines in retrospective analysis, with Tree-of-Thoughts achieving peak yields and Reflexion reducing computational cost via episodic memory.