Many people use AI for coding by placing the whole workflow inside one chat: describe the task, ask the agent to read the repository, edit files, run tests, and summarize the result. That works for small experiments. It becomes fragile in long-running projects, shared repositories, production systems, or professional software automation. The problem is not only whether the model is smart enough. The problem is that the model is being asked to own too much of the delivery process. The better pattern is to place AI capability inside a project-specific delivery pipeline. AI is the worker. The project pipeline constrains, validates, records, and escalates. Why project-specific matters A general AI tool cannot know a project's real risk boundaries by default. In a trading system, payout, KYC, funded accounts, order states, and production release are hard boundaries.…