Invoice Reconciliation Lab (with DataHub)
This lab is designed for in-person workshop events led by a Boomi instructor. Participants must be provisioned with access to a dedicated Boomi platform account during the workshop. This lab is not intended for self-led learning.
For self-paced learning, check out the Customer Support Triage (Extended) Lab.
Business Situation
Global-Corp Manufacturing, a mid-sized industrial parts manufacturer, processes thousands of invoices each month. Matching invoices against purchase orders (POs) and goods receipts (GRNs) is critical for maintaining supplier trust and financial accuracy. However, their current invoice reconciliation process is entirely manual and creating major operational bottlenecks.
When an invoice arrives from a supplier, an AP clerk must manually hunt for the corresponding Purchase Order from the ERP system and the Goods Received Note from the warehouse's digital records. They then visually compare all three documents line-by-line in spreadsheets to check for discrepancies in item codes, quantities, and prices. This tedious, time-consuming three-way match process is highly prone to human error, leading to delayed payments that strain supplier relationships and occasional overpayments that hurt the company's bottom line.
Challenges
- Manual 3-way matching is error-prone and time-consuming: AP clerks spend hours hunting through files, emails, and drives to locate Purchase Orders, manually comparing spreadsheets line by line, and dealing with the "Paper Chase" of tracking down delivery documents from the warehouse.
- Delays in detecting mismatches cause payment delays and strain supplier relationships: The slow manual process creates an "Email Maze" of routing approvals through email chains, leading to late payments that damage vendor trust and reliability.
- High workload delays monthly closing process: The sheer volume of invoices creates "Excel Chaos" where discrepancies are logged in endless email trails, and the "Data Entry" burden of manual ERP entry and double-checking slows down the entire accounts payable workflow. This "Exception Crisis" requires emergency calls and vendor escalations that disrupt operations.
- File Burden and audit trail maintenance: The need to organize documents and maintain comprehensive audit trails adds to the administrative overhead and complexity of the manual process.
Solution
An intelligent Invoice Reconciliation Agent takes on the role of a procurement specialist with autonomous decision-making capabilities. Rather than following a rigid workflow, the agent is equipped with data access tools and business rules that allow it to independently determine how to validate procurement documents. The agent has the expertise to collect relevant documents from DataHub, apply validation logic based on business requirements, identify discrepancies using its analytical capabilities, and take appropriate action—whether approving clean invoices or escalating issues to human reviewers. This role-based approach provides flexibility to handle variations in procurement scenarios while maintaining consistent compliance with business policies.
This lab teaches role-based agentic design rather than task-based workflow automation. Instead of telling the agent "do step 1, then step 2, then step 3," you'll define WHO the agent is (an Invoice Reconciliation Specialist) and WHAT capabilities it has. The agent will then autonomously determine the best approach for each situation it encounters.
This role-based approach creates a flexible, intelligent solution that can adapt to variations in procurement scenarios while maintaining operational efficiency and compliance.
Use Case
In this lab, you will build an intelligent AI agent on the Boomi platform that functions as an autonomous Invoice Reconciliation Specialist. Rather than programming a step-by-step workflow, you'll define the agent's role, provide it with the necessary capabilities, and establish boundaries within which it can operate independently.
You'll equip your agent with:
- Data access capabilities: DataHub Query tools to retrieve Purchase Orders, Goods Received Notes, and Invoices
- Business intelligence: Validation rules and criteria for three-way matching
- Action capabilities: Tools to update invoice status in DataHub and communicate findings via email
- Autonomous decision-making: The ability to determine its own approach for each reconciliation scenario
Business Outcomes
- Operational Continuity: Ensures timely, accurate payments to suppliers, preventing production delays and disruptions to the supply chain
- Control & Compliance: Enforces 3-way match to detect invoice errors, prevent fraud, and curb unauthorized spending
- Supplier Confidence: Strengthens trust and reliability with suppliers, reducing churn and delivery risks
Key Performance Indicators (KPIs)
- Invoice Processing Time: Measure the time from invoice receipt to payment approval, directly affecting cash flow and supplier satisfaction
- PO Compliance Rate: Track the percentage of purchases made through approved POs to reduce rogue spend and improve financial controls
- Early Payment Discounts Captured: Monitor the percentage of eligible discounts successfully realized through faster, more accurate invoice processing
Lab Structure
This hands-on workshop guides you through creating an intelligent Invoice Reconciliation Agent using role-based agentic design principles.
Part 1: Define Agent Role and Responsibilities
Define your agent's role as an Invoice Reconciliation Specialist by establishing its core purpose, domain expertise, and operational boundaries. You'll create two broad responsibility areas—data collection & validation, and analysis & reporting—that give your agent the autonomy to determine its own approach while maintaining business compliance through guardrails.
Part 2: Equip Agent with Data Access Capabilities
Provide your agent with the tools it needs to independently gather procurement information. You'll create DataHub Query tools and attach them to your agent's data responsibility area, allowing it to autonomously decide when and how to retrieve Invoice, Purchase Order, and GRN data based on each reconciliation scenario.
Part 3: Enable Agent Actions and Test Autonomy
Complete your agent's capabilities by providing tools to take action on its findings. You'll add status update and email notification capabilities, then test your agent to observe how it autonomously determines its approach to reconciliation—deciding which data to gather, how to validate it, and when to escalate issues to human reviewers.
Let's build your Invoice Reconciliation Agent! Click Next below to begin with the prerequisites.