A new framework for selecting pre-trained vision-language models for specific downstream tasks is proposed, using layer-wise conductance and directional conductance divergence to improve performance and outperform state-of-the-art baselines. This development is relevant to people building and operating AI agents as it addresses the challenge of selecting the optimal model for a given task, which is crucial for efficient and effective AI deployment.