The article outlines a tutorial series focused on building production-grade Retrieval-Augmented Generation (RAG) systems and AI agents. It emphasizes creating a full-stack enterprise knowledge application, highlighting essential components like secure data access and permissions management. Users are instructed on how to connect their third-party data sources safely through OAuth and permissions syncing, ensuring that access controls are enforced throughout the RAG pipeline. The tutorial stresses the importance of maintaining fresh and reliable data to keep responses accurate and suitable for production use. The second chapter dives into the complexities of permissions and access control, addressing various protocols and offering strategies to implement a robust permissions framework that can handle the intricacies of RAG systems. This is vital for safeguarding sensitive data while allowing the necessary flexibility for users to interact with their information. Subsequent sections tackle optimizing AI agent tool calling, which involves enhancing the efficiency of AI interactions with third-party applications. The article provides insights into why agents often struggle with scaling tool calls and outlines methods to improve this aspect. Lastly, it discusses building a workflow builder with AI actions, detailing the creation of user interfaces for executing workflows, integrating triggers, and enabling self-service debugging for users. This practical approach equips developers with the tools needed to create versatile and effective AI-driven workflows.