Can GNNs predict LLM workflow performance?
GNNs as Predictors of Agentic Workflow Performances
This paper proposes using Graph Neural Networks (GNNs) to predict the performance of workflows in multi-agent AI systems. This avoids the cost of repeatedly running the workflow with LLMs for evaluation during optimization. A new benchmark, FLORA-Bench, is introduced to evaluate this approach. Experiments demonstrate that GNNs can effectively predict workflow performance, are robust to changes in the driving LLM, and significantly speed up workflow optimization, although generalization across different task domains needs improvement. The key point for LLM-based multi-agent systems is that GNNs offer a faster, more efficient way to optimize complex agent workflows compared to relying solely on LLM evaluations.