MoFlow: Multi-Objective Agentic Workflow Generation
Moving beyond accuracy to balance cost, latency, and robustness in autonomous agent design.
We study the generation of agentic workflows that jointly optimize multiple objectives, such as accuracy, cost, latency, robustness, and consistency. Existing methods for workflow generation typically optimize accuracy alone or a weighted sum of objectives, so each trained genera...
ArXiv CS.AI
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MoFlow represents a critical shift from single-metric performance toward balanced, multi-dimensional optimization in autonomous agent systems. By treating competing constraints like cost and latency as first-class citizens, this approach enables the deployment of production-grade agents in resource-constrained environments.
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