I kept running into the same problem while building AI tooling: the smarter the assistant looked, the less predictable the product felt. You send a message because you want to continue the current coding session. The system decides you probably meant "start a new task," rewrites the intent, and suddenly you are no longer talking to the runtime you thought you were using. That sounds small until you try to use it every day. The problem was not model quality The failure mode had very little to do with whether the underlying executor was Codex or Claude Code. The real problem was control. In a coding workflow, there are at least two very different intents: I want to keep talking to the current runtime session. I want a higher-level assistant to look at the whole situation, choose what to do, and coordinate work for me. If those two paths share the same default entry point, the product starts guessing too much. That guess is expensive.…