MoFlow Generates Agentic Workflows Optimizing Multiple Objectives

VERA Brief

AI-generated. Grounded in the article and its cited sources.

Researchers have introduced MoFlow, a new system for generating agentic workflows that can optimize multiple objectives simultaneously. This approach allows for flexible trade-offs between objectives like accuracy, cost, and latency without requiring retraining.

Key facts

• MoFlow is a new method for generating agentic workflows that optimize multiple objectives.
• Objectives that can be optimized include accuracy, cost, latency, robustness, and consistency.
• Unlike current methods, MoFlow allows for flexible trade-offs without retraining.
• The system formulates workflow generation as a multi-objective Markov decision process.
• Evaluations on six benchmarks showed MoFlow achieving the highest average hypervolume.