AI-Powered E-Discovery Agent

Agent Description:

The AI-Powered E-Discovery Agent automates business data analysis by connecting to SQL databases containing sales, pricing, and supply chain information. It extracts, normalizes, and merges this data into a unified dataset. The agent then analyzes the dataset to detect anomalies, assess risks (such as pricing discrepancies or delivery delays), and identify performance trends. Finally, it generates actionable insights and structured summaries to support faster, data-driven decision-making.

Purpose and Components
  • Purpose: To automate the extraction and analysis of business data (sales, pricing, and supply chain) for anomaly detection, risk assessment, and performance insights, supporting faster and data-driven decision-making.
  • Components:

    • SQL Data Extractor: Retrieves data from structured sources such as Sales, Competitor Pricing, and Supply Chain databases.

    • Data Normalization Engine: Standardizes and merges multi-source datasets for unified analysis.

    • Insight Generator: Detects anomalies, trends, and performance issues.

    • Risk Evaluator: Flags potential risks in pricing, sales, or supply metrics.

Supported Capabilities

  • Connect to SQL databases and retrieve multi-table business data.

  • Extract product-level details such as ProductID, Category, UnitsSold, Revenue, and Risk Scores.

  • Normalize and merge data from different sources.

  • Detect anomalies and irregular performance patterns.

  • Generate actionable insights and summarize findings.

  • Present structured results in tabular or JSON format.

  • Continuously monitor updates across datasets.

LLM Used

  • GPT-5 (Default, inherits capabilities from parent orchestration agent)

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

Sub-Agents

1. Data Retrieval Agent

  • Role:Data Extractor
  • Scope:Retrieve and normalize multi-source SQL data for discovery analysis.
  • Description: This sub-agent connects to SQL databases and extracts data from tables such as SalesData, CompetitorPricing, and SupplyChainConditions. It aligns schema formats, standardizes values, and prepares a unified dataset for further analysis.
  • LLM Used: Default (inherits from parent).
  • Tools used: SQL Query Tool, Info SQL

2. Insight Generator Agent

  • Role:Risk Analyzer
  • Scope:Analyze structured data to detect anomalies and generate insights.
  • Description: This sub-agent receives structured datasets from the Data Retrieval Agent and performs analytical evaluations. It identifies pricing discrepancies, supply chain inefficiencies, and sales performance deviations, producing concise and actionable insights for decision-making.
  • LLM Used: Default (inherits from parent).
  • Tools used: SQL Query Tool
Tools Used:

  • SQL Query Tool: Executes SQL queries for multi-source data extraction.

  • Info SQL Tool: Retrieves metadata and schema details.

Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary

  1. Data Retrieval Agent connects to SQL sources and extracts relevant data.

  2. It normalizes and merges datasets into a consistent structure.

  3. The output is sent to Insight Generator Agent for analysis.

  4. The Insight Generator detects anomalies and produces insights.

  5. The agent outputs findings or confirms stable system performance.
Sample Questions:

  • Show recent anomalies in sales and supply data.

  • List high-risk products with delivery delays.

  • Which categories show a drop in performance?

  • Highlight competitor pricing variations.

  • Summarize current business performance trends.