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This paper proposes a unified framework for customizing and deploying LLM-based multi-agent systems in enterprise settings, combining model customization through continual pretraining, fine-tuning, and preference optimization with inference optimization using speculative decoding and FP8 quantization. It achieves 4.48x throughput speedup while maintaining performance on enterprise workloads.