Predictive Stock Recommender Agent

Agent Description:

The Predictive Stock Recommender is an AI-driven system designed to improve sales strategies and inventory planning by identifying the most frequently purchased electronic components. It analyzes historical purchase patterns, customer repeat behavior, and emerging trends to generate a ranked list of top-selling items. This enables businesses to prioritize popular components, improve product recommendations, and optimize cross-selling efforts.

Purpose and Components
  • Purpose: To analyze historical stock and purchase data to identify and recommend the most frequently bought components.
  • Components:
    • A data extractor to pull comprehensive purchase and stock data from databases.
    • An analytical engine that evaluates customer behavior and trends.
    • A recommendation generator that produces a ranked list of high-demand items.
Supported Capabilities
  • Extracting structured data from stock and purchase databases.
  • Analyzing purchase frequency, repeat buys, and sales trends.
  • Identifying regionally trending or high-demand components.
  • Filtering and ranking components based on real customer behavior.
  • Generating a top 5 product list for use in sales, marketing, or procurement.

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. Stock Data Extractor

  • Role:Data Extractor.
  • Scope:Retrieves data relevant to stock levels, customer purchases, technical indicators, and market news.
  • Description: This sub-agent connects to the database and pulls raw information about historical sales, stock levels, trends, and market dynamics. It does not analyze or summarize data but ensures that all records are cleanly formatted and properly labeled for analysis.
  • LLM Used: Google Vertex (inherits from parent).

2. Stock Research

  • Role:Stock Analyzer.
  • Scope:Processes the extracted data to uncover the top-selling components across time and user segments.
  • Description: This sub-agent evaluates stock and sales data across multiple dimensions:

    • Repeat customer behavior

    • Recent sales surges or dips

    • Regional demand patterns

      Based on this analysis, it generates a final, ranked list of the top 5 most frequently purchased components.

  • LLM Used: Google Vertex (inherits from parent).
Tools Used:
  • SQLquery tool: For executing SQL queries on purchase and stock tables.
  • SQLlist tool: For listing all available tables in the predictive stock database.
  • SQLinfo tool: For understanding column definitions and data structures within the database.
Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary
  • The Stock Data Extracter initiates the process by connecting to the database and retrieving historical purchase data, stock levels, and relevant market signals.
  • The structured output is passed to the Stock Research sub-agent.
  • The Stock Research agent performs multidimensional analysis using metrics such as purchase frequency, customer retention, and demand trends.
  • The agent synthesizes these findings into a final output: a ranked list of the top 5 most frequently purchased electronic components, ideal for guiding product recommendations, inventory prioritization, and sales campaigns.
Sample Questions:
  • What are the top 5 most frequently purchased components this month?
  • Which components should we keep in stock based on recent buying behavior?