Request for Quotation Processing (RFQ Agent)

Agent Description:

The RFQ Agent streamlines the procurement process by extracting structured requirements from unstructured RFQ documents and matching them against internal inventory. It can parse plain text or PDF RFQs, normalize and structure the item data, and propose suitable substitutes if requested items are unavailable. This ensures efficient RFQ processing, improves supplier response accuracy, and automates key steps in the quoting workflow.

Purpose and Components
  • Purpose: To automate the processing of RFQs by extracting structured item details and matching them against inventory, including alternatives when direct matches are unavailable.
  • Components:
    • A data extractor to structure item and delivery requirements from RFQs.
    • A matcher to check inventory and recommend substitutes.
    • A report generator to summarize availability and substitution logic.
Supported Capabilities
  • Parsing RFQ text or PDFs to extract item requirements.
  • Identifying and normalizing item details (name, quantity, certifications, delivery deadline).
  • Matching requested items against internal inventory.
  • Suggesting compatible substitutes with adequate quantity and certifications.
  • Producing readable markdown reports with availability status and rationale.
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. RFQ Requirement Extractor

  • Role:Data Extractor.
  • Scope:Parses RFQ text or PDFs to extract items, quantities, certifications, and delivery deadlines.
  • Description: This sub-agent processes RFQ inputs—either in plain text or extracted PDF text—and structures them as JSON:

    • Extracts per-item: Product Name, Quantity (normalized), Certifications.

    • Extracts global or per-item delivery deadlines.

    • Handles partial data by assigning null to missing fields.

    • Ensures values are clean and standardized (for example, removes units like pcs).

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

2. Inventory Matcher and Substitute Suggester

  • Role:Quote Responder.
  • Scope:Searches inventory for exact matches or proposes substitutes based on similarity and availability.
  • Description: This sub-agent accepts structured JSON from the extractor and interacts with an inventory database:

    • Performs case-insensitive exact match on item names.

    • If match found fetches quantity, certification, delivery timeline.

    • If not found applies fuzzy/approximate matching to propose a substitute with:

      • ≥75% quantity availability.

      • Compatible or superior certification.

    • Outputs a readable markdown report using status indicators:

      • Available

      • Suggested Alternative

      • No match found

  • LLM Used: Google Vertex (inherits from parent).
Tools Used:
  • MCP Client Tool: Used by RFQ Requirement Extractor to process PDF inputs and extract text.
Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary
  • The RFQ Requirement Extractor processes plain text or PDF-based RFQ inputs, identifies item names, quantities, delivery dates, and certification requirements, and structures the information in JSON format.
  • This structured output is passed to the Inventory Matcher & Substitute Suggester, which checks internal inventory for exact matches.
  • For unavailable items, it suggests substitutes based on similarity, availability, and certification compatibility.
  • The final output is a markdown-formatted report showing which items are available, which have suggested alternatives, and which could not be matched—enabling clear and immediate supplier response decisions.
Sample Questions:
  • Can you show me the full details of this RFQ, including items, quantities, and deadlines