Brand Sentiment Monitoring Agent

Agent Description:

The Brand Sentiment Monitoring Agentis an automated intelligence pipeline that tracks real-time brand mentions, stores them in a structured database, and evaluates risk levels. It identifies negative spikes in sentiment and triggers structured alerts for crisis management.

This agent enables organizations to:

  • Automate Mention Retrieval: Proactively crawl the web for complaints, news, and viral content using real-time search tools.

  • Standardize Data Ingestion: Convert unstructured search snippets into structured records (ID, Platform, Text, Source) for longitudinal tracking.

  • Calculate Sentiment Volatility: Automatically compute negative-to-positive ratios and severe negative counts.

  • Crisis Detection: Compare current data against historical thresholds to identify high-risk sentiment shifts.

  • Manage Response Escalation: Log alerts and provide decision-ready summaries for high-stakes PR scenarios.

Purpose and Components
  • Purpose: The purpose of this agent is to automatically monitor and assess brand health across digital channels by aggregating reviews, news mentions, and social feedback into a unified analysis. It provides real‑time sentiment evaluation and compares trends against historical benchmarks to distinguish routine noise from emerging reputation risks. By eliminating slow, manual tracking, the agent enables Marketing and PR teams to detect early warning signs, prevent potential PR crises, and respond proactively to protect brand reputation.
  • Components:

    • Query Intake Agent: Uses Google Search to identify brand reviews, news, and issues.

    • Database Intelligence Agent: Manages storage, JOIN operations, and aggregation of structured mentions.

    • Trend and Risk Evaluation Agent: Detects sentiment shifts and calculates negative ratios.

    • Alert and Reporting Agent: Manages escalation logging and delivers final summaries.

Supported Capabilities

  • Proactive web crawling for viral content and platform-specific mentions.

  • Conversion of unstructured snippets into structured SQL records (ID, Platform, URL).

  • Crisis detection using "Negative Ratio" thresholds (for example, >30% negative spikes).

  • Automated alert logging in an AlertLog table with specific escalation reasons.

LLM Used

  • OPENAI GPT_4O_MINI

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

Sub-Agents

1. Query Intake Agent

  • Role:Query Parser

  • Scope:Parses user requests and executes real-time web intelligence gathering.

  • Description:A REACT agent that identifies the brand and timeframe from a user query. It uses a Google Search Tool to fetch reviews, complaints, and news. It cleans and structures these mentions into a specific database-ready format before forwarding them.

  • LLM Used: Default (Inherits from parent agent).

2. Database Intelligence Agent

  • Role:Data Analyst

  • Scope:Manages the storage, retrieval, and aggregation of structured brand data.

  • Description: A REACT agent utilizing a SQL Database Toolkit to insert new mentions and perform complex JOIN operations. It aggregates counts of total and negative mentions to provide factual data points for risk evaluation.

  • LLM Used: Default (Inherits from parent agent).

3. Trend and Risk Evaluation Agent

  • Role:Risk Evaluator

  • Scope:Analyzes database results to detect sentiment shifts and risk levels.

  • Description:Calculates the "Negative Mention Ratio" and percentage changes over time. It compares current counts against preset thresholds (for example, >30% negative) to classify the brand state as Stable, Moderate, High, or Critical.

  • LLM Used: Default (Inherits from parent agent).

4. Alert and Reporting Agent

  • Role:Response Manager

  • Scope:Delivers final reports and manages escalation logging.

  • Description:Receives the risk summary and determines if a formal alert should be triggered. For High/Critical risks, it logs an entry in the AlertLog and prepares an escalation report; for Stable states, it provides a factual summary of current brand health.

  • LLM Used: Default (Inherits from parent agent).

Tools Used:

  • Google Search: Scours the live web for reviews, news, and viral "complaint" mentions related to the brand.

  • SQL Database Toolkit: Interacts with the brand_sentiment.sql database to log mentions and aggregate historical data.
Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary

User query (for example, "Check sentiment for 'TechBrand' over the last 48 hours").

  1. Detection & Collection:

    • The Intake Agent performs multiple targeted Google searches for reviews and issues.

    • It extracts and standardizes the results, filtering out duplicate URLs.

  2. Structured Storage:

    • Mentions are sent to the Database Agent, which records them into the BrandMentions table and links them to existing sentiment labels.

  3. Risk Calculation:

    • The Evaluation Agent asks the database for the total negative count.

    • Logic: If Negative_Ratio > 30% OR Severe_Negatives > 2 -> System enters High Risk state.

  4. Reporting and Alerts:

    • The Alert Agent checks the system state.

    • Action: If High/Critical, it logs the alert and provides an "Escalation Reason" to the user.

Sample Questions:

  • Give me all mentions mentioning product Z in a negative context.

  • Which products of Brand X are generating the most severe negative mentions?