Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications
Quick Answer
This paper shows that A unified framework for customizing and deploying multi-agent systems enhances enterprise applications by achieving a 4.48x throughput speedup while maintaining performance and robustness.
Quick Take
A unified framework for customizing and deploying multi-agent systems enhances enterprise applications by achieving a 4.48x throughput speedup while maintaining performance and robustness. The approach combines continual pretraining, supervised fine-tuning, and inference optimization techniques like FP8 quantization to address domain-specific needs and reduce latency costs.
Key Points
- Framework enables rapid domain adaptation for multi-agent systems in enterprise settings.
- Achieves 4.48x speedup in throughput while maintaining performance on complex tasks.
- Combines continual pretraining, supervised fine-tuning, and preference optimization.
- Integrates speculative decoding and FP8 quantization for cost-efficient serving.
- Addresses high latency and inference costs in agentic workflows.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 18502v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems demonstrate strong performance on complex reasoning and task execution, enabling broad enterprise applications. However, production deployment remains challenging due to domain-specific customization requirements and high latency and inference costs in agentic workflows. We propose a unified framework for customization and efficient deployment of multi-agent systems in real-world settings.
The first stage, Agentic Model Customization, combines continual pretraining, supervised fine-tuning, and preference optimization to adapt a compact model to specialized domains while retaining strong agentic capabilities. The second stage, Inference Optimization, integrates speculative decoding and FP8 quantization with targeted calibration to enable cost-efficient serving with minimal quality loss. Across enterprise workloads, our framework enables rapid domain adaptation and achieves a 4.
48x speedup in throughput while maintaining performance and improving robustness on long-tail scenarios.
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