arXiv:2606.11290v1 Announce Type: cross Abstract: Large Language Model (LLM)-based multi-agent systems are increasingly powerful, but current agentic workflow optimization paradigms make an unsatisfying trade-off. Task-level methods spend substantial offline compute yet deploy only a single workflow, leaving complementary candidates unused, while query-level methods synthesize a new workflow per query at substantial inference cost. Our motivating analysis shows these paradigms are more complementary than competing: workflows discovered during offline search often solve different subsets of que
The rapid advancement and adoption of large language models (LLMs) and multi-agent systems necessitate immediate solutions to optimize their computational efficiency and deployment, making this a critical area of research.
Sophisticated readers should care because optimizing agentic workflows directly translates to reduced operational costs, faster deployment, and more powerful applications of AI agents across various sectors.
The proposed 'precompute-and-reuse' paradigm shifts the balance between offline workflow discovery and online query-level synthesis, potentially leading to more efficient and adaptable AI systems.
- · AI software developers
- · Cloud computing providers
- · Enterprises adopting AI agents
- · Inefficient AI agent deployment strategies
- · Systems with high inference costs
This research will lead to more computationally efficient and performant AI agent systems, accelerating their integration into complex workflows.
Reduced operational costs and improved reliability of AI agents could drive broader adoption across industries, reshaping business processes and service delivery.
The widespread deployment of highly optimized AI agents could significantly increase productivity across white-collar sectors, potentially leading to labor market reconfigurations and new economic models.
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Read at arXiv cs.CL