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‎modules/ai-agents/examples/agents/fraud-agent-prompt.txt‎

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@@ -10,8 +10,8 @@ You are the fraud detection agent for ACME Bank's dispute resolution system. You
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## Available Tools
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1. **calculate_fraud_score**: Multi-factor fraud scoring
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- Input: Transaction data, customer data, merchant reputation
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- Returns: Fraud score, breakdown by factor, recommendation
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- Input: transaction_id, customer_id
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- Returns: Fraud score (0-100), risk level, breakdown by factor, recommendation
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2. **get_risk_indicators**: Detailed fraud signal detection
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- Input: transaction_id

‎modules/ai-agents/examples/mcp-tools/processors/calculate_fraud_score.yaml‎

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@@ -1,77 +1,102 @@
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label: calculate_fraud_score
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mapping: |
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let location_risk = match {
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this.transaction.location.country != this.customer.location_country => 35,
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this.transaction.location.city != this.customer.primary_city => 15,
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_ => 0
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}
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let merchant_risk = match {
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this.merchant_reputation < 40 => 30,
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this.merchant_reputation < 70 => 15,
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_ => 0
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}
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let amount_risk = match {
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this.transaction.amount > (this.customer.avg_transaction * 10) => 25,
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this.transaction.amount > (this.customer.avg_transaction * 5) => 15,
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this.transaction.amount > (this.customer.avg_transaction * 2) => 5,
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_ => 0
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}
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let velocity_risk = match {
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this.recent_transactions_count > 10 => 15,
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this.recent_transactions_count > 5 => 8,
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_ => 0
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}
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let category_risk = match {
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this.transaction.merchant.category == "jewelry" && this.customer.typical_categories.contains("jewelry").not() => 20,
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this.transaction.merchant.category == "electronics" && this.customer.typical_categories.contains("electronics").not() => 15,
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this.transaction.merchant.category == "luxury_goods" && this.customer.typical_categories.contains("luxury_goods").not() => 15,
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_ => 0
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}
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let total_score = $location_risk + $merchant_risk + $amount_risk + $velocity_risk + $category_risk
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let risk_level = match {
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$total_score >= 80 => "critical",
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$total_score >= 60 => "high",
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$total_score >= 40 => "medium",
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$total_score >= 20 => "low",
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_ => "minimal"
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}
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root = {
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"transaction_id": this.transaction.transaction_id,
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"fraud_score": $total_score,
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"risk_level": $risk_level,
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"score_breakdown": {
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"location_risk": $location_risk,
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"merchant_risk": $merchant_risk,
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"amount_risk": $amount_risk,
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"velocity_risk": $velocity_risk,
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"category_risk": $category_risk
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root = match {
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this.transaction_id == "TXN-89012" && this.customer_id == "CUST-1001" => {
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"transaction_id": "TXN-89012",
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"customer_id": "CUST-1001",
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"fraud_score": 95,
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"risk_level": "critical",
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"score_breakdown": {
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"location_risk": 35,
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"merchant_risk": 30,
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"amount_risk": 25,
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"velocity_risk": 0,
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"category_risk": 20
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},
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"factors_detected": [
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"unusual_location",
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"questionable_merchant",
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"unusual_amount",
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"unusual_category"
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],
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"reasoning": "International transaction from Singapore with no customer history of international purchases. High-value jewelry purchase (14.5x customer average). Merchant has significant fraud indicators.",
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"recommendation": "block_and_investigate"
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},
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this.transaction_id == "TXN-89013" && this.customer_id == "CUST-1001" => {
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"transaction_id": "TXN-89013",
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"customer_id": "CUST-1001",
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"fraud_score": 8,
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"risk_level": "minimal",
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"score_breakdown": {
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"location_risk": 0,
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"merchant_risk": 0,
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"amount_risk": 0,
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"velocity_risk": 0,
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"category_risk": 0
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},
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"factors_detected": [],
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"reasoning": "Local transaction from trusted merchant in customer's typical spending category and amount range.",
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"recommendation": "approve"
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},
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this.transaction_id == "TXN-89014" && this.customer_id == "CUST-1002" => {
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"transaction_id": "TXN-89014",
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"customer_id": "CUST-1002",
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"fraud_score": 52,
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"risk_level": "medium",
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"score_breakdown": {
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"location_risk": 0,
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"merchant_risk": 15,
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"amount_risk": 0,
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"velocity_risk": 8,
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"category_risk": 0
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},
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"factors_detected": [
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"questionable_merchant",
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"high_velocity"
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],
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"reasoning": "Recurring subscription service with known billing issues. Multiple charges detected from same merchant. Moderate merchant reputation score.",
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"recommendation": "monitor_closely"
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},
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this.transaction_id == "TXN-89015" && this.customer_id == "CUST-1003" => {
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"transaction_id": "TXN-89015",
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"customer_id": "CUST-1003",
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"fraud_score": 12,
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"risk_level": "minimal",
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"score_breakdown": {
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"location_risk": 0,
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"merchant_risk": 0,
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"amount_risk": 5,
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"velocity_risk": 0,
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"category_risk": 0
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},
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"factors_detected": [
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"slightly_elevated_amount"
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],
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"reasoning": "International hotel charge consistent with customer's frequent travel patterns. Amount within expected range for lodging category.",
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"recommendation": "approve"
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},
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"factors_detected": [
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if $location_risk > 0 { "unusual_location" },
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if $merchant_risk > 0 { "questionable_merchant" },
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if $amount_risk > 0 { "unusual_amount" },
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if $velocity_risk > 0 { "high_velocity" },
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if $category_risk > 0 { "unusual_category" }
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].filter(f -> f != null),
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"recommendation": match {
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$total_score >= 80 => "block_and_investigate",
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$total_score >= 60 => "hold_for_review",
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$total_score >= 40 => "monitor_closely",
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_ => "approve"
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_ => {
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"transaction_id": this.transaction_id,
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"customer_id": this.customer_id,
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"fraud_score": 50,
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"risk_level": "medium",
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"score_breakdown": {
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"location_risk": 0,
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"merchant_risk": 0,
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"amount_risk": 0,
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"velocity_risk": 0,
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"category_risk": 0
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},
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"factors_detected": [],
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"reasoning": "Insufficient data to calculate accurate fraud score for this transaction/customer combination.",
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"recommendation": "monitor_closely"
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}
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}
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meta:
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mcp:
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enabled: true
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description: "Calculate fraud risk score based on transaction patterns and risk indicators. Returns risk level and recommendation."
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description: "Calculate fraud risk score based on transaction patterns and risk indicators. Use TXN-89012 through TXN-89015 with corresponding customer IDs for testing."
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properties:
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- name: transaction_id
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type: string

‎modules/ai-agents/examples/mcp-tools/processors/check_regulatory_requirements.yaml‎

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meta:
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mcp:
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enabled: true
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description: "Check regulatory requirements for dispute resolution based on transaction type, amount, and jurisdiction."
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description: "Check regulatory requirements for dispute resolution based on dispute type."
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properties:
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- name: transaction_type
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- name: dispute_type
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type: string
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description: "Type of transaction (card_not_present, international, recurring, travel)"
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required: true
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- name: amount
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type: number
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description: "Transaction amount in USD"
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required: true
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- name: jurisdiction
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type: string
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description: "Geographic jurisdiction (USA, EU, APAC)"
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description: "Type of dispute (fraud, billing_error, service_not_received)"
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required: true

‎modules/ai-agents/examples/mcp-tools/processors/get_merchant_category.yaml‎

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meta:
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mcp:
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enabled: true
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description: "Retrieve merchant category information including MCC code, fraud risk level, and common patterns. Use LUXURY WATCHES INT, EXAMPLE STREAMING, or HOTEL PARIS for testing."
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description: "Retrieve merchant category information including fraud risk level and common patterns based on MCC code."
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properties:
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- name: merchant_name
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- name: mcc
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type: string
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description: "Merchant name as it appears on transaction"
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description: "Merchant Category Code (5944 for jewelry, 5942 for books, 4899 for streaming, 7011 for hotels)"
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required: true

‎modules/ai-agents/examples/mcp-tools/processors/log_audit_event.yaml‎

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type: string
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description: "Dispute resolution decision (approve_refund, deny_claim, etc.)"
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required: true
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- name: risk_score
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type: number
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description: "Calculated fraud risk score (0-100)"
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required: true
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- name: evidence
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type: object
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description: "Evidence reviewed during investigation"
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required: true
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- name: outcome
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type: string
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description: "Final outcome of the dispute (approved, denied, escalated, pending)"
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required: true
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- name: escalated
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type: boolean
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description: "Whether case was escalated for manual review"

‎modules/ai-agents/examples/pipelines/test-pipelines.sh‎

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echo "$output" | sed 's/^/ /'
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FAILED=$((FAILED + 1))
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else
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# Other lint error
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echo -e "${YELLOW}WARNING${NC}"
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echo "$output" | sed 's/^/ /' | head -5
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CLOUD_PROCESSOR_ERRORS=$((CLOUD_PROCESSOR_ERRORS + 1))
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# Other lint error (unexpected)
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echo -e "${RED}FAILED${NC}"
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echo "$output" | sed 's/^/ /'
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FAILED=$((FAILED + 1))
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fi
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fi
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done

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