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Quote Parts Lab (with DataHub)

This is an Instructor-Led Lab

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.

Looking for Self-led learning?

For self-paced learning, check out the Customer Support Triage (Extended) Lab.

Business Situation​

Precision Source Manufacturing is a rapidly growing company that excels in producing custom electronic components. However, their sales process is being held back by a significant bottleneck: the quoting process for custom orders. Currently, when a customer submits a Bill of Materials (BOM), the sales team must embark on a slow, manual journey to generate a quote. This involves manually cross-referencing the customer's part descriptions with the internal Product Information Management (PIM) system to find matching components, then checking various supplier portals and internal systems for real-time pricing and inventory data. The quoting process is slow and impacts order delivery time, requiring a person to manually check supplier pricing and inventory across multiple systems to build a quote.

Challenges​

  • Manual BOM Reconciliation: Sales teams spend hours reconciling BOMs with supplier systems, leading to delays.
  • High Manual Effort: High manual effort reduces scalability and increases the risk of lost opportunities.
  • Extended Quote Turnaround: The entire process can take days, sometimes even weeks, leading to a frustratingly long sales cycle.
  • Competitive Disadvantage: This delay not only impacts sales velocity but also puts Precision Source Manufacturing at a competitive disadvantage as they lose deals to more agile competitors who can deliver quotes faster.

Solution​

Build an AI agent that acts as a skilled Quote Specialist—not just an automation that performs a single task, but a digital colleague that can handle the entire quoting process. The agent autonomously analyzes BOMs, researches across PIM and DataHub systems, and generates comprehensive quotes that address the customer's needs.

Use Case​

In this lab, you'll build an AI Agent using the principles of Architecting for Agents—evolving from the traditional Integration Mindset (building rigid, task-focused automations) to the Agentic Mindset (designing role-based digital colleagues).

Your Quote Specialist Agent will:

  1. Understand complex inputs—parsing Bills of Materials (BOMs) in various formats (XML, CSV, etc.)
  2. Research across multiple systems intelligently—querying PIM for product details and DataHub for live pricing and inventory
  3. Handle flexible data variations—recognizing part number variations and matching them to internal records
  4. Synthesize comprehensive responses—combining information from multiple sources into a complete quote
  5. Operate autonomously in business processes—activating automatically when new BOMs are received
The Role-Based Advantage

Traditional Task-Based Automation: Would require rigid input formats and fail if a part number didn't match exactly, often requiring manual intervention.

Your Role-Based Agent: Will identify the intent, attempt to match parts using fuzzy logic or description matching, and provide a best-effort quote with highlights for any unmatched items, just like a human specialist would.

This is the power of designing for roles, not just tasks!

Business Outcomes​

  • Reduce quote turnaround time from weeks to minutes through automated research and generation
  • Improve quote accuracy with AI-generated matches that leverage comprehensive data
  • Scale sales capacity without adding headcount by handling routine quoting autonomously
  • Increase win rates through faster, more responsive customer service

Key Performance Indicators (KPIs)​

  • Quote Turnaround Time: Reduce from weeks to minutes
  • Quote-to-Order Conversion Rate: Increase by 15%
  • Sales Team Productivity: Increase quotes per rep by 50%

Workshop Structure​

This hands-on workshop is divided into three parts that progressively build your understanding of Architecting for Agents:

Part 1: Create Tools for Your Quote Specialist Agent​

Build flexible tools that demonstrate role-based design principles:

  • Parts DataHub Tool - Aggregates unified parts data across systems
  • PIM Classification Tool - Surfaces pricing and availability information

You'll learn how optional parameters and clear descriptions give agents the flexibility to use tools in multiple ways—unlike traditional integrations that require rigid, predefined inputs.

Part 2: Build Your Quote Specialist Agent​

Design and configure your Agentstudio agent as a Quote Specialist role with:

  • Role definition that enables complex reasoning
  • Tasks that group tools by capability (research, match, quote)
  • Guardrails for safe operation (protecting sensitive pricing data)

You'll learn how to design for a role (Quote Specialist) instead of a task (lookup part)—enabling your agent to solve the entire quoting problem.

Part 3: Embed Your Agent into Business Processes​

Complete the transformation by integrating your agent into an event-driven Boomi process:

  • Integration Layer vs Reasoning Layer architecture
  • Event-driven activation where the agent is triggered by new BOMs
  • End-to-end flow from BOM receipt to quote generation

You'll learn how to apply agents at the right architectural layer—as the "reasoning engine" of your business process.

Ready to Get Started?

Let's build your Quote Parts Agent! Click Next below to begin with the prerequisites.