BOM Analyzer Agent

Agent Description:

The BOM (Bill of Materials) Analyzer Agent is an AI-driven tool designed to validate, interpret, and enhance electronics BOMs by parsing structured PDF data and providing insights into compatibility, sourcing, cost analysis, and system integration. It ensures accuracy in part specifications, suggests improvements, and checks real-world availability, especially useful for electronics manufacturing and procurement teams working with distributors like Avnet or Digi-Key.

Purpose and Components
  • Purpose: To automate BOM validation, suggest compatible or updated alternatives, analyze cost and performance metrics, and ensure sourcing feasibility for each listed component.
  • Components:
    • A BOM data extractor for parsing and structuring component lists from PDF BOM files.
    • An intelligent analyzer to check compatibility, cost, and availability.
    • A query responder to handle user questions and sourcing decisions.
Supported Capabilities
  • Extracting and normalizing BOM data from PDFs into structured formats.
  • Identifying component specs, categories, and pricing details.
  • Flagging missing or ambiguous fields for review.
  • Responding to technical and sourcing queries (for example, compatibility, power draw).
  • Performing logical system validations (for example, CPU-Motherboard, RAM type, PSU wattage).

Providing sourcing suggestions and cost breakdowns for assembled systems.

LLM Used
  • Google Vertex
    Note: To learn more about the LLM and to modify its behavior, refer to the Configuring LLM settings section.

Sub-Agents

1. BOM Data Extractor

  • Role:Data Extractor.
  • Scope:Processes electronics BOMs typically used by distributors like Avnet or Digi-Key.
  • Description: This sub-agent scans and processes BOM files (for example, PDF) to extract the following fields per component:

    • Part Name, Part Number, Category (for example, CPU, GPU, RAM), Manufacturer, Quantity, Unit Price, Total Price, and Specifications (for example, socket type, wattage).

    • Normalizes brand names, merges duplicates, and ensures all specifications are consistently formatted.

    • Flags missing or ambiguous data entries for manual review.

    • Outputs the structured data in JSON format ready for downstream analysis.

  • LLM Used: Google Vertex (inherits from parent).
  • Tool Used: MCP Client Tool

2. BOM Analyzer Agent

  • Role:Compatibility Analyst.
  • Scope:Handles user questions regarding compatibility, power consumption, total system cost, availability, and sourcing tips.
  • Description: This sub-agent receives structured BOM data and supports complex BOM-level analysis:

    • Validates CPU–motherboard socket compatibility, RAM standards, PSU wattage sufficiency, PCIe slot matches, and so on.

    • Calculates total system cost for custom quantities (for example, 10 assembled systems).

    • Responds to sourcing queries such as:

      • Which parts are out of stock?

      • Are there deprecated or end-of-life components?

  • Returns formatted responses with reasoning, references to specific components, and optional sourcing tips (for example, links to Digi-Key, Newegg).
  • LLM Used: Google Vertex (inherits from parent).
Tools Used:
  • MCP Client Tool: Used by the BOM Data Extractor to process and structure component tables from PDFs.
Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary
  • The BOM Data Extractor receives a BOM PDF and parses each listed component into a structured format.
  • It extracts key fields like part name, number, manufacturer, category, quantity, prices, and specifications. It also normalizes brand names, merges duplicates, and flags any missing fields.
  • The structured JSON data is passed to the BOM Analyzer Agent.
  • The BOM Analyzer Agent answers technical queries (for example, Is this GPU compatible with the motherboard?), performs system-level calculations (for example, What’s the wattage draw?), and flags availability or deprecated parts.
  • The agent returns responses in a clear, rich reference format, optionally including sourcing links or tips to assist in procurement decisions.
Sample Questions:
  • Are there deprecated or end-of-life components?
  • Which parts are out of stock?