We have all been there. You spend hours meticulously crafting the perfect system prompt or tool description for your AI agent. It performs beautifully in your initial tests. But a week later, production data throws a curveball. The team's coding standards shift, edge cases emerge, or the underlying LLM updates, and suddenly your agent's performance degrades. To fix it, you have to manually inspect the logs, diagnose the failure pattern, rewrite the prompt, and run manual tests. This is an open-l