ChatGPT Work – Multi-Step Deliverables, Delegation, and Supervision
Lesson 1: From Answers to Actions — Understanding Agentic Work
Lesson Objectives
By the end of this lesson, students should be able to:
- Distinguish conversational generation from Work-style multi-step execution
- Describe planning, tool use, observation, and iteration loops
- Identify tasks suited and unsuited to ChatGPT Work, Codex, or human-only review
- Define success and stopping conditions
Lesson Content
A conversational assistant mainly produces an answer. ChatGPT Work or an agentic system can choose tools, gather information, manipulate files, navigate interfaces, and continue across multiple steps toward a goal. The basic loop is: understand the goal, plan, act with a tool, observe the result, update the plan, and stop when success or a boundary is reached.
Good Work tasks have a clear outcome, observable state, bounded permissions, and reversible intermediate steps. Poor tasks involve vague authority, irreversible consequences, missing success criteria, or decisions requiring values and accountability the user has not supplied.
Agentic does not mean autonomous in the human sense. The system remains constrained by available tools, credentials, context, policies, and its fallibility. Users should expect uncertainty, interruptions, and requests for clarification or approval.
Enroll to continue this lesson.
The preview above shows the lesson objectives and opening lesson content. Enroll to view the full lesson, complete the practice work, and take the lesson quiz.