Lesson 1547 of 2116
Building a dry-run mode for AI agents that touch production
Let agents plan and explain destructive actions without performing them, then approve in one click.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2dry run
- 3human in the loop
- 4preview
Concept cluster
Terms to connect while reading
Section 1
The premise
Agents earn trust by showing what they will do before doing it.
What AI does well here
- Render the planned tool calls as a human-readable diff
- Block irreversible actions until approval
What AI cannot do
- Predict every side effect a tool may cause
- Replace a real test environment
Understanding "Building a dry-run mode for AI agents that touch production" in practice: AI agents can take actions, run loops, and call tools — giving one instruction can start a chain of automated steps. Let agents plan and explain destructive actions without performing them, then approve in one click — and knowing how to apply this gives you a concrete advantage.
- Apply dry run in your agentic workflow to get better results
- Apply human in the loop in your agentic workflow to get better results
- Apply preview in your agentic workflow to get better results
- 1Design an agent spec: goal, tools, permissions, stop condition
- 2Run a simple web-search agent in a sandbox environment
- 3Instrument an existing workflow to identify where an agent could save time
Key terms in this lesson
End-of-lesson quiz
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