Evidence-Driven Development: Give Your Coding Agent Something to Prove

Evidence-Driven Development (EDD) offers a practical methodology for working with AI coding agents by shifting the focus from simple code generation to verifiable outcomes. The approach requires developers to define clear, observable claims about application behavior and identify specific conditions that would disprove them. By building a small task list application, the author demonstrates how to create a rigorous feedback loop where AI-generated code is tested against these claims using subprocesses and negative controls. This ensures that 'done' means more than just passing tests; it means the software behaves as promised under real-world conditions. The article emphasizes that evidence must remain inspectable and independent of the agent's own summaries. By implementing an 'exercise gate' to verify that scenarios were actually executed, developers can ensure their coding agents produce reliable, reproducible results, ultimately creating a more robust and transparent development workflow.