Deploying AI agents powered by OpenAI's o1 models, which excel in complex reasoning tasks, can lead to substantial costs and operational challenges due to hidden reasoning tokens that are difficult to track. The o1 models prioritize reasoning over immediate response generation, which can delay outputs and complicate monitoring and debugging processes, as their internal deliberations remain opaque. This opacity contributes to difficulties in controlling costs and ensuring model reliability, with industry surveys indicating that a significant portion of enterprises struggle to achieve measurable ROI from their AI investments. To address these issues, a set of nine best practices is proposed, including defining clear outcome contracts, structuring prompts for stepwise reasoning, grounding models with context boundaries, specifying tools and success criteria, and implementing reasoning-friendly patterns. These practices aim to enhance the control, efficiency, and reliability of AI deployments by making reasoning processes more transparent and manageable. The text also highlights the role of modern observability platforms like Galileo in providing comprehensive evaluation and monitoring infrastructure, ensuring that AI systems operate within defined boundaries while maintaining compliance and performance standards.