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Remittance Reconciliation Lab

Business Situation​

Global Manufacturing Corp is a mid-sized enterprise with hundreds of customers and thousands of invoices processed monthly. Their Accounts Receivable department is drowning in payment discrepancies—customers frequently short-pay invoices due to damaged goods, pricing disputes, or quantity adjustments, then send unstructured remittance advice documents via email to explain the deductions.

The current process is entirely manual and fragmented. When a $9,000 payment arrives in the banking portal for a $10,000 invoice, the system flags it as an exception. Meanwhile, the customer's email explaining the $1,000 deduction for damaged goods sits in a separate inbox. An AR specialist must manually discover both events, open the email, download and read the PDF remittance advice, cross-reference the invoice in the ERP system to verify the claim, calculate whether the deduction is valid, create a credit memo, post the cash receipt, update the general ledger, and notify the customer—all while juggling dozens of similar cases.

This creates a significant bottleneck. The truth about each payment is scattered across three disconnected systems: the bank deposit data, the unstructured customer email, and the invoice details in the ERP. Traditional task-based integrations fail here because they're designed to match exact amounts, not to reason about why amounts don't match or synthesize information from multiple asynchronous sources.

Challenges​

  • Fragmented data across disconnected systems: Payment data lives in banking portals, remittance explanations arrive via email, and invoice details exist in the ERP—no system connects them
  • Unstructured remittance documents: Customers send PDFs, scanned images, or plain-text emails in various formats that rigid parsers cannot process
  • Manual exception handling bottleneck: Every payment discrepancy creates a manual investigation task, overwhelming AR staff
  • Asynchronous event correlation: Bank deposits and customer emails arrive at different times, requiring human pattern recognition to link them
  • Time-consuming verification process: AR specialists must manually cross-reference claims against invoice line items to validate deductions
  • Delayed cash application: Payments sit unapplied for days while staff investigates discrepancies, impacting cash flow visibility
  • Inconsistent resolution policies: Without clear thresholds, similar cases are handled differently by different AR specialists
  • No autonomous resolution capability: Even simple, validated discrepancies require manual credit memo creation and GL posting

Solution​

Build an AI agent that acts as a skilled Accounts Receivable Specialist—not just an automation that flags exceptions, but a digital colleague with the authority to synthesize information from multiple asynchronous sources, validate payment discrepancies autonomously, and resolve them within defined governance thresholds.

Use Case​

In this lab, you'll build an AI Agent using the principles of role-based agentic design—evolving from task-focused exception handling to a role-based digital colleague that can reason about complex, multi-system payment scenarios.

Your Accounts Receivable Agent will:

  1. Monitor asynchronous event streams from banking portals and email systems—detecting payment discrepancies and correlating them with customer remittance explanations
  2. Extract information from unstructured sources—reading email attachments, parsing PDF remittance advice, and handling natural language explanations without rigid data formats
  3. Synthesize truth from multiple systems—linking bank deposits to customer emails to ERP invoice data to understand the complete picture
  4. Validate payment discrepancies intelligently—cross-referencing customer claims against invoice line items to verify that deductions match actual goods or services
  5. Apply governance thresholds autonomously—determining whether discrepancies fall within authorized resolution limits (e.g., under $2,500)
  6. Execute financial transactions with authority—creating credit memos, posting cash receipts, and updating the general ledger without human intervention
  7. Notify stakeholders transparently—sending resolution confirmations to customers and internal audit trails to AR management
  8. Escalate complex cases appropriately—routing discrepancies above thresholds or with insufficient documentation to human specialists
The Autonomous Resolution Advantage

This lab demonstrates a core principle from agentic architecture: truth synthesis over single-source matching.

Traditional Task-Based Automation: When payment_amount ≠ invoice_total, create an exception flag and stop. The payment data and the email explanation remain disconnected, requiring a human to manually bridge the gap.

Your Role-Based Agent: Understands that the "truth" is synthesized. It autonomously correlates the $9,000 bank deposit with the customer's email claiming "$1,000 of damaged goods," verifies the claim against the invoice line items, and resolves the transaction end-to-end—producing a single, closed invoice in the General Ledger without human intervention.

This is the power of designing for roles with reasoning authority, not just tasks with rigid rules!

Business Outcomes​

  • Reduce payment resolution time by 70-85% through autonomous discrepancy investigation and resolution
  • Improve cash application speed by eliminating manual exception queues and immediately applying valid payments
  • Increase AR specialist productivity 5-10x by handling routine discrepancies autonomously and escalating only complex cases
  • Enhance audit compliance with full-trace observability of every resolution decision and automated documentation

Key Performance Indicators (KPIs)​

  • Average Payment Resolution Time: Measure time from payment receipt to final GL posting (target: reduce from days to minutes)
  • Autonomous Resolution Rate: Percentage of payment discrepancies resolved without human intervention (target: 60%+ for sub-threshold cases)
  • Days Sales Outstanding (DSO): Track improvement in cash application speed and its impact on working capital (target: reduce by 15-25%)
  • Exception Queue Volume: Monitor reduction in manual AR workload (target: 70%+ reduction in flagged exceptions)
  • Audit Accuracy Rate: Measure correctness of autonomous resolutions through periodic human review (target: 95%+ accuracy)

Workshop Structure​

This hands-on workshop is divided into three parts that progressively demonstrate role-based agentic design for financial operations:

Part 1: Create Tools for Your Accounts Receivable Agent​

Build five specialized tools that demonstrate flexible, role-based capabilities:

  • Read Email Attachment - Extracts information from unstructured PDF remittance advice using OCR and document intelligence
  • Get Bank Transaction Details - Retrieves payment information from the banking portal API
  • Get Invoice Details - Fetches invoice line items and totals from the ERP system
  • Create Credit Memo - Generates credit memos in the ERP for validated discrepancies
  • Post Cash Receipt - Applies payments to invoices and updates the general ledger

You'll learn how to design tools that handle unstructured data and give agents the flexibility to use them in multiple reasoning sequences—unlike traditional integrations that require perfectly formatted inputs.

Part 2: Build Your Accounts Receivable Agent​

Design and configure your Agentstudio agent as an Accounts Receivable Specialist role with:

  • Role definition that enables multi-source truth synthesis and autonomous financial decision-making
  • Tasks that group tools by capability (investigate, validate, resolve, document)
  • Conversation starters that demonstrate handling payment discrepancies with asynchronous context
  • Guardrails for safe financial operations (governance thresholds, escalation rules, audit transparency)
  • Instructions that implement financial controls (verification logic, approval thresholds, compliance documentation)

You'll learn how to grant an agent the authority to execute transactions within defined governance boundaries—enabling autonomous resolution while maintaining financial control.

Part 3: Embed Your Agent into Business Processes​

Complete the transformation by integrating your agent into an event-driven financial workflow:

  • Understand how asynchronous event correlation works across banking and email systems
  • See the agent synthesize truth by linking disconnected data sources (bank deposits, customer emails, ERP invoices)
  • Experience governance-driven autonomy where the agent resolves within thresholds and escalates beyond them
  • Observe end-to-end financial automation from payment receipt to GL posting without rigid, pre-defined paths
  • Review the complete audit trail showing the agent's reasoning path for compliance and review

You'll learn how to architect agents as monitors and governors of financial processes—synthesizing information, validating transactions, and executing resolutions with full transparency.

Ready to Get Started?

Let's build your Accounts Receivable Agent using role-based agentic principles! You're not just building an exception handler—you're building a digital colleague that can reason across systems, validate financial claims, and autonomously resolve complex payment scenarios.

Click Next below to begin with the prerequisites.