Example on AI Agent using Pega Infinity: Product Return Processing

As a part of this weekend activity, I asked Claude to give me the steps to build a simple AI Agent using Pega Infinity platform. For this exercise, Claude give me an idea on the agent which combines rule-based logic with Predictive Analytics and NLP to deliver a automated solution. Not sure how far these steps can give us right result but I like this approach to build the agent in the realtime biz. Those who are interested can take a look at these 10 steps and plan to build this agent using Pega platform 👍

Simple Example: Product Return Processing AI Agent

This AI agent will:

• Receive product return requests
• Evaluate eligibility based on company policy
• Suggest appropriate actions (approve, deny, escalate)
• Generate appropriate responses to customers
• Track and log outcomes

Step 1: Define Your AI Agent Requirements

Business Problem: Streamline the product return process by automating eligibility checks and routine decisions.

Business Objectives:

• Reduce manual review time by 75%
• Maintain 95% accuracy in decision-making
• Improve customer satisfaction through faster responses

Step 2: Create a new case type

1. Log into Pega Infinity and navigate to App Studio
2. Click "Case Types" → "Create"
3. Name: "Product Return Request"
4. Identifier: "PRR-"
5. Define case lifecycle stages: Initiation (Receive request) Validation (Check eligibility) Decision (Approve/Deny/Escalate) Fulfillment (Process return) Closure (Complete return)

Step 3: Configure data model

• Create these case properties:
• - OrderNumber (Text)
• - PurchaseDate (Date)
• - ReturnReason (DropDown)
• - ProductCondition (DropDown)
• - CustomerTier (Text)
• - ReturnEligibility (Boolean)
• - ReturnScore (Decimal)
• - AgentDecision (Text)
• - CustomerResponse (Text).
• Create a data page to fetch order information: Name: "D_OrderDetails" , Object type: "Order", Source: Connect to your order management system via connector

Step 4: Design Case stages and steps

Initiation Stage:

1. Create a step "Receive Return Request"
2. Add a form to capture return details
3. Add a data transform to populate case properties

Validation Stage:

1. Create a step "Verify Order Details" Add a connector to validate order from OrderNumber Configure service call to the order management system
2. Create a step "Check Return Eligibility" Add a decision table to determine basic eligibility Use case properties like PurchaseDate and ReturnReason

Decision Stage:

1. Create a step "Calculate Return Score" Add a decision rule to assign a score based on multiple factors
2. Create a step "Determine Action" Add a decision split with three paths: Approve, Deny, Escalate Configure routing based on ReturnScore

Fulfillment Stage:

1. Create steps for each decision path "Process Approval" for approved returns "Handle Denial" for denied returns "Escalate to Manager" for complex cases

Closure Stage:

1. Create a step "Notify Customer"
2. Create a step "Update Inventory"
3. Create a step "Close Case"

Step 5: Implement Decision Rules

1. Navigate to Decision Designer
2. Create a decision table "DT_ReturnEligibility" with the logic : 1) IF PurchaseDate < (Today - 30 days) THEN ReturnEligibility = false, 2) IF ProductCondition = "Damaged" THEN ReturnEligibility = true, 3) IF ReturnReason = "Changed Mind" AND PurchaseDate > (Today - 14 days) THEN ReturnEligibility = true
3. Create a decision tree "DT_ReturnScore": Root: ReturnEligibility If false: ReturnScore = 0 If true: Branch on CustomerTier Branch on ReturnReason Branch on ProductCondition Assign ReturnScore values (0-100)

Step 6: Add Predictive Analytics

1. Go to Prediction Studio
2. Create a predictive model "PM_ReturnApproval": Input: Historical return data (import CSV with past returns) Target: Approval outcome Features: PurchaseDate, ReturnReason, ProductCondition, CustomerTier
3. Train and test model
4. Apply model to calculate probability of approval
5. Connect model to case through a data transform

Step 7: Implement NLP Capabilities

1. Create text analyzer rule "TA_ReturnReason": Configure patterns to detect common return reasons Add sentiment analysis to detect customer frustration
2. Create text analyzer rule "TA_ProductIssue": Configure patterns to identify specific product issues Add entity extraction to identify product names/models

Step 8: Configure Agent Automation

1. Create SLA "SLA_ReturnProcessing": Set goal: 24 hours for decision Set deadline: 48 hours for complete processing
2. Configure work queues: "AutomatedReturns" for AI-processed cases "ManualReview" for escalated cases
3. Set up automated assignment rules: Route based on ReturnScore Route based on product category

Step 9: Test your AI Agent

1. Create test cases with sample data: Valid return within policy Invalid return (too old) Edge case (exactly 30 days) High-value customer with exception
2. Execute test scenarios and verify: Decision accuracy Response generation Routing logic SLA compliance

Step 10: Deploy and Monitor

1. Create a dashboard to monitor: Volume of returns processed Approval/denial rates Average processing time Escalation frequency
2. Configure alerts for: Processing delays Unusual approval/denial patterns Customer satisfaction scores dropping

Implementation Example: Decision Rule for Return Eligibility

Here's a more detailed look at implementing the decision rule in Pega Decision Designer:

1. Navigate to Decision Designer
2. Create a new Decision Table
3. Name it "DT_ReturnEligibility"
4. Configure the conditions: Column 1: PurchaseDate Column 2: ReturnReason Column 3: ProductCondition Column 4: CustomerTier
5. Define the action: Set ReturnEligibility
6. Create rules like:

P.S: These steps need to be refined once the implementation starts and this article is just an idea on building the AI agent using Pega platform.

Pictures Credit: https://claude.ai