Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications
arXiv: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
As multi-agent systems move from research to enterprise application, the challenges of customization and efficient deployment in real-world settings become critical bottlenecks that must be addressed for widespread adoption.
This framework offers a path to overcome practical hurdles in deploying multi-agent AI, potentially enabling their broad integration into enterprise workflows and collapsing existing software layers.
The focus shifts from purely academic performance to practical, scalable deployment strategies for multi-agent systems, including methods for cost-effective customization and efficient inference.
- · Enterprises adopting AI
- · AI platform providers
- · Developers of multi-agent systems
- · SaaS providers reliant on manual workflows
- · Companies slow to integrate AI automation
Enterprises can more easily integrate custom multi-agent AI systems, streamlining complex operations and reducing operational costs.
Increased adoption of AI agents leads to a significant re-architecting of enterprise software, potentially disintermediating traditional IT services.
The widespread deployment of highly customized and efficient AI agents could fundamentally alter labor markets and business processes across multiple industries.
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