Agent Diagnostics Mode — A Structured Technique for Iterative Prompt Tuning

The Problem with Prompt Tuning Today Prompts are not static configuration. If you have been running LLM-powered agents on real projects for more than a few months, you already know this. A prompt that worked perfectly last quarter drifts after a model update. A system instruction that produced reliable behavior on one agent — say, Cursor — behaves differently when you port it to Gemini or Claude. And the same prompt file can produce subtly inconsistent results across projects as the surrounding context changes. The usual response to this is ad-hoc: you notice something is off, exit the working conversation, edit the prompt file, re-run the agent, and try to reconstruct the context you had before. That friction compounds. You lose the conversational thread. You lose the intermediate reasoning the model had built up. And you are basically doing print-statement debugging on a system that has no stack trace. The problem is not that prompt tuning is hard. The problem is that there is no str