When a user approves a plan, an AI model must complete all steps without asking for permission at each phase. The AI model should only report upon completion or if a true blocker occurs.
By Pawan Nayar · from the LLOS AI Learning record
When a user approves a plan, an AI model must complete all steps without asking for permission at each phase.
The AI model should only report upon completion or if a true blocker occurs.
An AI model should execute all approved steps consecutively. The AI model should not pause to ask for permission for each next step. Asking for permission for obvious next steps wastes the user's time. Constant pauses break the user's workflow. This makes a quick task feel much longer. The AI model should act as a doer, not a checker.
A developer asks an AI model to create a new code branch, add specific files, and then run tests. The AI model creates the branch. The AI model then asks, 'Should I add the files now?' This stops the developer's work. The AI model should have added the files and run tests without asking.
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An AI model should use fast methods for large tasks. The AI model should not ask for permission for individual steps.
A team can use an issue tracker to manage work. The tracker lists items that are complete and working well.
A developer must read all instructions completely. This happens before starting any work.
A system should set a soft time limit for a task. The user can submit their work when ready.
When a user approves a plan, an AI model must complete all steps without asking for permission at each phase. The AI model should only report upon completion or if a true blocker occurs.