AI can already help you get work done, but do you still have to stay nearby and repeatedly say, “Keep going” or “Check it again”? This course shows you how to turn those repeated prompts into a reliable loop: when to start, how to verify results, how to recover from failure, and when to stop. No programming experience is required. You will begin with a small, low-risk task and work through the design, execution, and review of a loop yourself.
Distinguish ordinary human-AI turns, an agent's internal Agent Loop, and outer Loop Engineering, then judge whether a multi-turn AI process has the essential conditions of a reliable loop.
Use repetition frequency, clarity of acceptance, failure risk, and implementation cost to decide whether a real task is suitable, partly suitable, or unsuitable for a loop, and place the automation boundary on specific actions.
Rewrite a vague request as a minimum loop specification with an explicit trigger, goal, tools, verification, boundaries, and stop conditions, so every iteration advances from real evidence.
Choose the smallest sufficient design among turn-based, goal-driven, scheduled, and proactive loops based on trigger style, lifecycle, verification conditions, and required control.
Run a small, low-risk, finite goal-driven loop and use a baseline, iteration-by-iteration verification, and a completion report to prove why it continued and why it stopped.
Use loop evidence to locate a problem in the Prompt, Context, Harness, Loop, or implementation layer, then turn one failure into a correctly placed improvement that can be regression-tested.
