This content originally appeared on DEV Community and was authored by Prema Ananda

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

What I Built

I created BrandGuard AI — a comprehensive multi-agent brand reputation monitoring system that fundamentally transforms how companies track and respond to online mentions. This isn’t just another social media monitoring tool — it’s an intelligent, fully automated platform that processes brand mentions through a sophisticated AI pipeline.

The system addresses a critically important problem: in today’s hyper-connected digital world, brand mentions spread across countless platforms with such intensity that human teams physically cannot track them effectively. Traditional methods are too slow, too manual, and lack the depth to separate critical signals from noise.

BrandGuard AI solves this through:

• Automatic collection of meaningful brand mentions from major social platforms (Twitter, Reddit, LinkedIn) using reliable BrightData data streams with simple mechanisms for adding new sources
• Intelligent analysis of each significant mention through Google Gemini AI to extract sentiment, urgency level, business impact, and crisis indicators
• Automated report generation with daily and weekly strategic summaries that typically require hours of analytical work
• Instant access via a conversational Slack bot that understands natural queries like “Show me critical mentions for today” or “Show me what’s up with Apple today”
• Real-time insights delivery through a dynamic web dashboard with interactive visualizations

The system’s uniqueness lies in its multi-agent architecture — each key function (data collection, analysis, reporting, user interface) is handled by a specialized, independent agent built as separate n8n workflows. This modular design ensures exceptional resilience, scalability, and maintainability.

Demo

Live Demo: BrandGuard AI
GitHub Repository:

graph TD
    subgraph "Data Collection

The system is fully functional and deployed in a production environment.
The complete source code includes all n8n workflows, Docker configurations and setup documentation.

n8n Workflow

The BrandGuard AI system is built on a modular architecture of interconnected n8n workflows. Each workflow performs a specialized function in the overall data processing pipeline.

Core Workflows:

1. Data Collection and Processing:

• Brand Monitoring Step 1 – schedules data collection from BrightData and creates processing tasks
  

• Brand Monitoring Step 2 – normalizes raw data into unified format through adapters

• Brand Monitoring Step 3 – performs AI analysis through Google Gemini and saves to MongoDB

2. AI Reporting:

• AI Daily Summary – generates daily analytical summaries
• AI Weekly Summary – creates weekly strategic reports

3. User Interfaces:

• BrandGuard AI - Dashboard – web dashboard with interactive analytics

• BrandGuard AI bot – Slack bot with natural language support

• Brand Mention Report – detailed mention viewing

Key Architectural Features:

• Modularity: each agent is an independent workflow
• Extensibility: adapter pattern for easy addition of data sources
• Resilience: centralized error handling and state monitoring
• Scalability: task management through MongoDB jobs collection

Complete workflow JSON files: available in the /workflows folder of the GitHub repository

Technical Implementation

AI Agent Configuration

The system uses Google Gemini as the primary AI model through the standard node in n8n. Each agent is configured with specialized system instructions for specific tasks:

Mention Analysis Agent:

• System Instructions: “Analyze brand mentions and return structured JSON with fields: sentiment_score, confidence, urgency_level (1-10), key_topics, crisis_indicators, business_impact, severity, recommended_action”
• Model: Google Gemini Flash and Pro for deep contextual understanding
• Memory: Stateless – each mention is analyzed independently
• Tools: JSON parser for structured output

Slack Bot Agent:

• System Instructions: “You are an assistant for brand reputation analysis. Interpret user queries and translate them into MongoDB queries. Available functions: help, today, brand, search, critical, summary”
• Model: Google Gemini Flash for natural language processing
• Memory: Session context within a single request
• Tools: MongoDB connector, Slack message formatting

Reporting Agents:

• System Instructions: “Create executive summary based on brand mentions for specified period. Include trends, critical moments, recommendations”
• Model: Google Gemini Pro for insight generation
• Memory: Aggregated data for the period
• Tools: MongoDB aggregation pipeline

Bright Data Verified Node

BrightData is the foundation of the entire data collection pipeline in BrandGuard AI. I used the official verified BrightData node in n8n for integration with their data ecosystem:

Setup and Configuration:

• Datasets used: Twitter Posts, Reddit Posts, LinkedIn Posts with ability to add new ones
• Filtering: by brand keywords, engagement metrics (likes, reposts)
• Frequency: automatic collection every n hours through n8n scheduler
• Data format: JSON with rich metadata (author, time, engagement metrics)

Key BrightData Advantages:

• Data Quality: structured, clean data without need for complex preprocessing
• Reliability: stable data delivery without gaps or duplication
• Scalability: handling large volumes of mentions without infrastructure burden
• Compliance: all data collected in compliance with platform ToS

Architectural Solution with Adapters: I implemented an adapter pattern for working with different BrightData sources. Each source (Twitter, Reddit, LinkedIn) has its adapter in the Brand Monitoring Step 2 workflow, which normalizes the specific data structure into a unified format. This allows easy addition of new datasets from the BrightData marketplace without changes to the core system logic.

Journey

The development of BrandGuard AI was an iterative process that started with a simple idea of automating mention monitoring and evolved into a comprehensive multi-agent system.

Stage 1 – MVP (Minimum Viable Product): Started with a basic n8n workflow that collected data from one source and saved to MongoDB. Quickly realized that a more complex architecture was needed to handle heterogeneous data sources.

Stage 2 – Architectural Design: Decided on a multi-agent architecture after studying the limitations of the monolithic approach. Divided the system into specialized agents with clear areas of responsibility.

Stage 3 – Slack Bot Implementation: Getting the Slack bot to work properly was the main challenge. It required significant effort to set up the proper webhooks, authentication, and message handling to create a seamless conversational experience.

Major Challenges and Solutions

Challenge 1: Heterogeneous Data Sources

• Problem: each platform (Twitter, Reddit, LinkedIn) returns data in different formats
• Solution: implemented adapter pattern using Switch node in n8n, where each source has its adapter for data normalization

Challenge 2: AI Analysis Reliability

• Problem: Google Gemini sometimes returned invalid JSON or missed fields
• Solution: added JSON validation, retry logic, and fallback values in n8n Code nodes

Challenge 3: Processing Scalability

• Problem: as data volume increased, the system began to slow down
• Solution: implemented job-based architecture with state tracking in MongoDB and asynchronous processing

Challenge 4: Slack Bot User Experience

• Problem: wanted natural conversation with the bot, not rigid commands
• Solution: used Google Gemini for NLU (Natural Language Understanding), allowing the bot to understand free-form questions

What I Learned

Technical Insights:

• Power of n8n as orchestrator: n8n proved to be the ideal tool for creating complex multi-agent systems thanks to visual programming and rich integration ecosystem
• Importance of data quality: working with BrightData showed the critical importance of clean, structured data for AI analysis
• AI prompt engineering: creating effective prompts for structured output is an art requiring multiple iterations

Architectural Lessons:

• Modularity above all: dividing the system into independent agents significantly simplified development, debugging, and scaling
• Importance of state monitoring: job-based architecture with tracking in MongoDB enabled creation of a transparent and debuggable system

This project showed me how modern no-code/low-code platforms can be used to create enterprise-level AI solutions with minimal time and resource investment.

P.S.
I want to note that before this project, I had never worked with n8n and didn’t even know about BrightData‘s existence. This challenge became a real discovery of two outstanding products.

n8n impressed me with its flexibility and power of visual programming. What seemed like a complex task of orchestrating multiple AI agents turned into an intuitive process of creating interconnected workflows.

BrightData turned out to be exactly the missing link needed for quality data collection. The reliability, structure, and richness of their datasets dramatically simplified the system architecture.

Thanks to DEV.to for organizing this challenge! Without it, I would never have discovered these wonderful products and wouldn’t have been able to create BrandGuard AI. This is an excellent example of how technical competitions promote learning new technologies and creating innovative solutions.

This content originally appeared on DEV Community and was authored by Prema Ananda