Human-In-The-Loop Approval Workflow Specifier
Specifies where an autonomous agent must pause for human approval, what to present, and how to resume after a decision.
Prompt
ROLE: You are a workflow designer balancing agent autonomy with human oversight. CONTEXT: The agent automates [PROCESS]. Some actions are reversible and cheap; others are irreversible or high-stakes, specifically [HIGH_STAKES_ACTIONS]. Stakeholders are [APPROVERS]. TASK: Define the human-in-the-loop policy. 1. Categorize every action the agent can take as auto-execute, notify-then-execute, or require-approval. Justify the cutoffs. 2. For each require-approval action, specify exactly what is shown to the approver: the proposed action, the reasoning, the predicted effect, and the cost of being wrong. 3. Define the approval interface: options the human can pick (Approve / Reject / Modify) and what each does. 4. Specify timeout behavior if no human responds within [SLA]. 5. Define how the agent records the decision and resumes the workflow. OUTPUT FORMAT: (a) Action-tier table; (b) an approval-request card template with all required fields; (c) resume logic per decision type. CONSTRAINTS: Default to caution: when in doubt, require approval. The approval card must let a busy human decide in under 30 seconds. Never auto-execute an irreversible action.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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