arXiv:2606.00189v1 Announce Type: new Abstract: Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-shelf agentic patterns. However, studies of fielded agentic systems show that production systems focus much more on issues such as simplicity, controllability, and predictability of inference costs. In this paper we propose principled approaches to designing and optimizing practical agentic systems. We describe an agent framework that enables designers to enforce modularity in agentic systems, by def
The rapid development and deployment of LLM-based systems drives an immediate need for practical, controllable, and cost-effective agentic designs beyond academic prototypes.
This research addresses fundamental challenges in AI agent deployment, enabling robust and scalable autonomous systems critical for white-collar automation and new AI applications.
The focus shifts from purely optimizing agentic performance to incorporating practical considerations like simplicity, controllability, and predictable inference costs, making real-world deployment more feasible.
- · AI software developers
- · Enterprises adopting AI agents
- · Cloud infrastructure providers
- · Companies with inefficient AI architectures
- · Manual workflow providers
Improved reliability and cost-effectiveness of AI agents lead to wider enterprise adoption.
Reduced operational costs and increased automation in various industries, impacting white-collar employment patterns.
The development of highly specialized and interconnected agentic systems that can manage complex tasks across diverse domains autonomously.
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Read at arXiv cs.LG