Eval Driven Development (EDD) is an innovative approach that adapts the traditional Test-Driven Development (TDD) methodology to leverage the capabilities of Large Language Models (LLMs), focusing on the creation of evaluations or "evals" to define desired behaviors before coding. This approach is exemplified through the integration of Claude Code with Model Context Protocol (MCP) servers, which provide the AI agent with access to relevant external data, enhancing its ability to perform evals accurately. By using these tools, developers can guide AI agents effectively through meta-prompting, allowing them to understand tasks and access resources such as the Eval Protocol library and GitHub repositories. The process begins with setting up a project environment and creating initial tests, which are then expanded into a comprehensive suite using AI-driven test generation, significantly saving manual effort. This evolution in the TDD workflow transforms the developer's role from writing code to defining high-level goals, while the AI handles detailed implementation, fostering a collaborative feedback loop that enhances the development and scaling of AI agents. Fireworks advocates for this shift towards AI-driven validation, seeing it as a future-oriented approach in the development of intelligent systems.