This episode focuses on the crucial role of Evals (evaluation methodologies) and MCP (Model Context Protocol) in the advancement of trustworthy and useful artificial intelligence. Evals are presented as systematic procedures for measuring various dimensions of AI models, ensuring trust, quality, and alignment with safety regulations through objective measurements and continuous monitoring. Concurrently, MCP is described as the operating fabric that enables AI models to interact with external data and tools, facilitating seamless integration, efficiency, and scalability within enterprise environments. The article emphasizes that the combined implementation of both Evals and MCP leads to responsible scaling, accelerated innovation with accountability, and broad societal benefits by providing both rigorous assessment and robust operational frameworks for AI systems.