Agentic workflows play a crucial role in the development of complex AI systems, yet their analysis and optimization remain challenging due to intricate interdependencies among components. The article from arXiv highlights these difficulties, emphasizing the need for effective methods to understand and improve such workflows. To address this, the authors propose using the Shapley value, a concept from cooperative game theory, as a potential solution. The Shapley value offers a systematic way to attribute contributions fairly among different agents within the workflow. This approach aims to provide clearer insights into the roles and impacts of individual components, facilitating better optimization strategies. By applying the Shapley value, researchers hope to overcome existing challenges and enhance the efficiency of agentic workflows. This proposal marks a promising step toward more transparent and effective AI system development.