human-in-the-loop

Original：🇺🇸 English

Translated

A hybrid pattern where the system pauses execution to request human approval, input, or disambiguation before proceeding with critical actions. Use when user asks to "add human approval", "require human review", "human-in-the-loop", or mentions approval workflows, human oversight, or escalation.

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Sourcelauraflorentin/skills-marketplace

Added on2026-05-02

NPX Install

npx skill4agent add lauraflorentin/skills-marketplace human-in-the-loop

SKILL.md Content

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Human-in-the-Loop

Human-in-the-Loop (HITL) bridges the gap between full automation and manual control. It treats the human user as a "privileged tool" or "approver". This is crucial for high-stakes domains where AI errors are unacceptable, or for ambiguous tasks where human intuition is required to guide the agent.

When to Use

High Consequence: Transferring money, deploying code to production, sending emails to customers.
Ambiguity Resolution: When the user's intent is unclear ("Book me a flight" -> "Which day?").
Active Learning: Collecting human feedback to improve the model (RLHF).
Quality Assurance: Reviewing the final draft of a report before publication.

Use Cases

Approval Workflow: Agent drafts a reply -> Human approves/edits -> Agent sends.
Escalation: Chatbot handles simple queries -> Escalates to human support for complex issues.
Clarification: Agent: "I found 3 files. Which one do you mean?" -> Human selects.

Implementation Pattern

python

def hitl_workflow(user_request):
    # Step 1: Plan/Draft
    plan = agent.create_plan(user_request)
    
    # Step 2: Risk Assessment
    if is_high_risk(plan):
        # Step 3: Pause for Approval
        # Send notification to user UI
        approval = notify_user(
            message="Agent wants to execute the following plan:",
            payload=plan
        )
        
        if approval.status == "REJECTED":
            return "Action cancelled by user."
        elif approval.status == "EDITED":
            plan = approval.new_plan
            
    # Step 4: Execute
    return agent.execute(plan)

Examples

Input: "Require human approval before the agent sends any email."