Design a small loop for an AI task that requires repeated checks and revisions. Decide whether automation is worthwhile, define the goal, acceptance checks, and stop conditions, then run a low-risk task and review the results. No programming experience is required.
适合人群
People who use ChatGPT, Claude, or Codex but still have to supervise every round of revisions
People who need repeatable ways to organize content, process data, or check engineering work
First-time users of agents or automation who want to start with a low-risk task
解决的问题
After AI produces a first version, you still handle the checks, corrections, and follow-up prompts
The task repeats often, but you are unsure whether automation is worthwhile
You worry about endless retries, changes outside the allowed scope, or unsupported claims of completion
A reminder fixes the immediate problem, but the same failure recurs on the next task
学完收获
Distinguish ordinary chat, an agent’s internal loop, and the outer loop that manages a task
Decide what to automate based on task frequency, ease of verification, risk, and cost
Define the trigger, goal, tools, evidence of completion, safety limits, and stop conditions
Choose the simplest suitable loop type, and explain what triggers each round and when it ends
Run a small, low-risk task, record each round, and explain why the work should continue or stop
Use failure records to identify the cause and decide what belongs in rules, tools, or tests
课程亮点
Work through six lessons on choosing a task for a loop, designing the loop, running it, and reviewing the results
Compare when to use turn-based, goal-driven, scheduled, and proactive loops
Check understanding through flashcards, questions about your reasoning, and records of actual runs
Start with a narrow scope, a limited number of iterations, and human control
