{
  "response_data": {
    "boolean": true,
    "boolean_label": "✅ Approve Post"
  }
}

def validate_boolean_response(response_data, response_config):
    """Validate boolean response against configuration"""
    
    if not isinstance(response_data, dict):
        return False, "Response must be an object"
    
    if "boolean" not in response_data:
        return False, "Missing boolean field"
    
    boolean_value = response_data["boolean"]
    
    # Validate boolean type  
    if not isinstance(boolean_value, bool):
        return False, "Value must be true or false"
    
    # Check required
    if response_config.get("required", False) and boolean_value is None:
        return False, "Boolean decision is required"
    
    # Validate associated label exists
    if "boolean_label" not in response_data:
        return False, "Missing boolean_label field"
    
    return True, "Valid"

# Usage example
is_valid, error_message = validate_boolean_response(
    response_data={
        "boolean": True,
        "boolean_label": "✅ Approve Post"
    },
    response_config={
        "required": True
    }
)

# Use boolean responses in sequence for complex workflows
def create_progressive_approval_workflow(content_id):
    """Create multi-stage boolean approval process"""
    
    # Stage 1: Initial safety check
    safety_check = create_boolean_request(
        loop_id="safety_team",
        request_text=f"Is content {content_id} safe for general audiences?",
        true_label="✅ Content is Safe",
        false_label="⚠️ Content Needs Review"
    )
    
    # Stage 2: Quality assessment (only if safety approved)
    if safety_check["response"]["boolean"]:
        quality_check = create_boolean_request(
            loop_id="quality_team", 
            request_text=f"Does content {content_id} meet quality standards?",
            true_label="⭐ High Quality",
            false_label="📝 Needs Improvement"
        )
        
        # Stage 3: Final publication decision
        if quality_check["response"]["boolean"]:
            final_approval = create_boolean_request(
                loop_id="editorial_team",
                request_text=f"Approve content {content_id} for publication?",
                true_label="🚀 Publish Now",
                false_label="📅 Schedule for Later"
            )

# Multiple boolean responses for consensus decisions
def build_consensus_decision(request_text, reviewer_count=3):
    """Create multiple boolean requests for consensus building"""
    
    requests = []
    for i in range(reviewer_count):
        request = create_boolean_request(
            loop_id="consensus_loop",
            request_text=request_text,
            true_label="👍 Approve",
            false_label="👎 Reject"
        )
        requests.append(request)
    
    # Wait for all responses
    responses = wait_for_all_responses(requests)
    
    # Calculate consensus
    approvals = sum(1 for r in responses if r["response"]["boolean"])
    consensus_threshold = len(responses) // 2 + 1  # Majority
    
    return {
        "decision": approvals >= consensus_threshold,
        "approval_count": approvals,
        "total_reviewers": len(responses),
        "consensus_strength": approvals / len(responses)
    }

# Use boolean responses to evaluate A/B test results
def evaluate_ab_test_results(test_id, variant_a_metrics, variant_b_metrics):
    """Human evaluation of A/B test statistical significance"""
    
    request_text = f"""
    Based on these A/B test results, should we proceed with Variant B?
    
    **Test Duration:** 14 days
    **Sample Size:** {variant_a_metrics['users']} vs {variant_b_metrics['users']} users
    
    **Variant A (Control):**
    - Conversion Rate: {variant_a_metrics['conversion_rate']:.2%}
    - Revenue per User: ${variant_a_metrics['revenue_per_user']:.2f}
    - User Satisfaction: {variant_a_metrics['satisfaction']:.1f}/10
    
    **Variant B (Test):**
    - Conversion Rate: {variant_b_metrics['conversion_rate']:.2%}
    - Revenue per User: ${variant_b_metrics['revenue_per_user']:.2f}
    - User Satisfaction: {variant_b_metrics['satisfaction']:.1f}/10
    
    **Statistical Significance:** {calculate_statistical_significance(variant_a_metrics, variant_b_metrics)}
    """
    
    return create_boolean_request(
        loop_id="data_science_team",
        request_text=request_text,
        true_label="📊 Deploy Variant B",
        false_label="🔄 Keep Current Version"
    )