Demand Forecasting Agent

Agent Description:

The Demand Forecasting AI Agent is a sophisticated system designed to predict future product demand. It works by analyzing data from multiple sources, including historical sales transactions, the impact of major industry events, and overarching market or seasonal trends. By combining these different datasets, the agent can generate a precise and consolidated forecast, helping businesses make informed decisions about inventory, sales strategies, and resource allocation based on a comprehensive view of market dynamics.

Purpose and Components
  • Purpose: To accurately forecast future demand for products.
  • Components:
    • Historical sales data (transactions, revenue).
    • Industry event data (promotions, seasonal events).
    • Market trend data (emerging technologies, growth factors).
Supported Capabilities
  • Extracting and cleaning sales data from a database.
  • Analyzing the impact of industry events on different product categories.
  • Identifying seasonal patterns and technology-driven trends.
  • Standardizing, merging, and summarizing data from multiple sources.
  • Generating compact and readable demand forecasts.
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. Sales Reader Agent

  • Role:Data Reader.
  • Scope: Extracts sales transaction data as would be logged in systems used by electronics distributors like Avnet or Digi-Key.
  • Description: This agent connects to the database and reads the transactions table to extract key sales details such as product ID, sale date, quantity, and revenue. It cleans and prepares the data for the final forecasting analysis.
  • LLM Used: Google Vertex (inherits from parent).
  • Tools used: MCP Client Tool

2. Industry Reader Agent

  • Role:Data Extractor.
  • Scope: Uses online search queries to find recent market events, promotions, or technology trends, and summarizes their potential impact on specific product categories.
  • Description: This agent focuses on the EventImpact table to extract promotional events and assess their effect on product categories. It also utilizes Google search capabilities to fetch relevant trends that influence market behavior and prepares the structured event insight data for analysis.
  • LLM Used: Google Vertex (inherits from parent).
  • Tools used: Google Search Tool

3. Seasonality Reader Agent

  • Role:Seasonality Analyst.
  • Scope: Extracts trend-driven investment data that reflects how emerging technologies impact various product categories from the given database.
  • Description: This agent connects the market trends table to identify seasonality and emerging technology impacts. It cleans and structures this data to feed into the demand forecasting process.
  • LLM Used: Google Vertex (inherits from parent).
  • Tools used: MCP Client Tool

4. Demand Forecasting Agent

  • Role:Demand Forecaster.
  • Scope:Takes the outputs from the three reader agents and: standardizes date and numeric formats, combines relevant insights, and outputs a compact, readable, and precise dataset or summary.
  • Description: This final agent in the pipeline collects structured data from the Sales, Industry, and Seasonality Readers, standardizes all formats (dates, currency, units), removes redundancies, and merges the insights to produce a clear, unified demand forecast for stakeholders.
  • LLM Used: Google Vertex (inherits from parent).
Tools Used:
  • MCP Client Tool – Used by Sales Reader and Seasonality Reader for PDF/data parsing.
  • Google Search Tool – Used by Industry Reader to retrieve up-to-date industry trends.
Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary
  • The Sales Reader Agent extracts and cleans historical sales transaction data using the MCP Client Tool.
  • The Industry Reader Agent extracts internal promotional data and uses Google search to identify current industry trends that affect product categories.
  • The Seasonality Reader Agent pulls data from market trend tables using the MCP Client Tool and identifies seasonal or tech-driven investment shifts.
  • The Demand Forecasting Agent gathers all the cleaned and structured outputs, standardizes formats (e.g., currency, dates), merges datasets, removes duplication, and generates a final, concise demand forecast summary.
Sample Questions:
  • How do seasonal promotions affect demand trends across products?
  • What is the forecasted demand for Product A in Q4 2025?