In deploying AI agents, challenges often arise between testing and production, primarily due to context management issues rather than model defects. These problems manifest as the agent's memory limitations, inconsistent responses to identical queries, infinite loops, lost task focus, and unrecoverable crashes. Solutions include using vector databases for efficient memory recall, ensuring deterministic context assembly, implementing workflow-level observability, maintaining explicit state checkpoints, and enabling recovery mechanisms for partial failures. The key to effective debugging and reliable production involves breaking the AI workflow into observable, manageable steps that offer transparency and control over each operation, allowing for systematic troubleshooting and enhancements.