CaseStudies

Multi-Agent Marketing Automation

Status: 📋 Planned
Version: Future
Last Updated: November 15, 2025

Overview

Parallel analysis and engagement at scale: Find 200+ Twitter accounts (VCs, customers), analyze historical data per account, automated replies to followers, competitor activity analysis across Twitter, Discord, GitHub with key datapoint extraction using one agent per data point for maximum parallel processing.

Description

Goal

Demonstrate MassGen’s scalability by orchestrating 200+ parallel agents for comprehensive social media marketing, competitive intelligence, and automated engagement at unprecedented scale.

Key Features

1. Account Discovery & Analysis
   • Identify target accounts (VCs, potential customers, influencers)
   • Analyze historical tweets, engagement patterns
   • Profile interests, posting schedule, interaction style
   • One agent per account for parallel processing
2. Automated Engagement
   • Intelligent reply generation based on account analysis
   • Personalized responses to followers
   • Timing optimization for maximum engagement
   • Sentiment-aware interactions
3. Competitor Intelligence
   • Monitor competitor Twitter activity
   • Track Discord community discussions
   • Analyze GitHub repository activity
   • Extract key datapoints (features, pricing, releases)
4. Data Aggregation & Reporting
   • Coordinator agent aggregates findings from 200+ workers
   • Generate insights: trending topics, sentiment analysis
   • Identify opportunities: interested prospects, partnership leads
   • Create actionable reports for sales/marketing teams
5. Scalability Features
   • Map-reduce architecture: one agent per data point
   • Efficient API quota management across agents
   • Rate limiting coordination
   • Fault tolerance: individual agent failures don’t stop pipeline

Architecture

Coordinator Agent
    ↓
Account Discovery (10 agents)
    ↓
Parallel Analysis (200+ agents, one per account)
    ├─ Agent 1: Account A analysis
    ├─ Agent 2: Account B analysis
    ├─ ...
    └─ Agent 200: Account ZZ analysis
    ↓
Aggregation & Reporting (Coordinator)
    ↓
Engagement Actions (as needed)

Testing Guidelines

Test Scenarios

1. Small Scale Test (10 accounts)
   • Setup: 10 Twitter accounts, 1 agent per account
   • Test: Analyze history, generate engagement strategy
   • Expected: Complete analysis in <5 minutes
   • Validation: All profiles accurately analyzed
2. Medium Scale Test (50 accounts)
   • Setup: 50 accounts across Twitter, Discord, GitHub
   • Test: Full analysis and engagement recommendations
   • Expected: Complete in <15 minutes with parallel execution
   • Validation: 5x faster than sequential execution
3. Full Scale Test (200+ accounts)
   • Setup: 200 Twitter accounts, competitor monitoring
   • Test: Complete marketing intelligence pipeline
   • Expected: Complete in <30 minutes
   • Validation: 10x+ speedup vs. sequential, all accounts processed
4. Engagement Quality Test
   • Setup: Generate replies for 20 diverse accounts
   • Test: Review generated content for quality and personalization
   • Expected: Each reply is personalized, contextually appropriate
   • Validation: Human evaluation: >80% approval rate
5. Competitor Intelligence Test
   • Setup: 5 competitor companies (Twitter + Discord + GitHub)
   • Test: Extract key datapoints across all platforms
   • Expected: Comprehensive competitive analysis
   • Validation: Findings match manual research
6. API Rate Limiting Test
   • Setup: 200 agents making Twitter API calls
   • Test: Execute without hitting rate limits
   • Expected: Intelligent quota management, no failures
   • Validation: All requests succeed, no rate limit errors

Use Case Testing

VC Outreach:

• Identify 50 VCs interested in AI/ML
• Analyze their investment history and interests
• Generate personalized outreach messages
• Track engagement and follow-ups

Customer Engagement:

• Find 100 users discussing pain points your product solves
• Analyze their needs and priorities
• Provide helpful responses (not spammy)
• Convert discussions to leads

Competitive Intelligence:

• Monitor 10 competitors across all platforms
• Track product launches, pricing changes
• Analyze community sentiment
• Identify market gaps and opportunities

Validation Criteria

• ✅ Successfully process 200+ accounts in parallel
• ✅ 10x+ speedup vs. sequential execution
• ✅ <2% failure rate for individual agent tasks
• ✅ Generated engagement content quality >80% approval
• ✅ Zero rate limit violations with intelligent quota management
• ✅ Actionable insights from aggregated data

Implementation Notes

Technical Requirements

APIs & MCPs:

• Twitter MCP (or Twitter API v2)
• Discord MCP for community monitoring
• GitHub MCP for repository analysis
• Data aggregation tools

Infrastructure:

• Parallel agent execution framework
• Rate limiting coordinator
• Centralized API quota management
• Result aggregation system

Configuration Example

marketing_automation:
  coordinator:
    backend: gpt-4o
    role: Orchestrator and aggregator
  
  workers:
    count: 200
    backend: gemini-2.0-flash  # Cost-effective for parallel tasks
    pattern: map-reduce
    one_per_datapoint: true
  
  data_sources:
    - twitter
    - discord
    - github
  
  rate_limiting:
    twitter_rpm: 450  # Twitter API v2 rate limit
    coordination: enabled
    backoff: exponential
  
  tasks:
    - account_discovery
    - historical_analysis
    - engagement_generation
    - competitor_monitoring

Execution Command

massgen --config marketing_automation_200_accounts.yaml \
  --query "Analyze 200 AI startup founders on Twitter, generate engagement strategy"

Related Work

• Map-Reduce Document Processing (Planned) - Similar parallel pattern
• Advanced Orchestration Patterns (Planned) - Parallel coordination
• Twitter Integration (Blocked) - Waiting for API access resolution

References

This case study demonstrates MassGen’s core value proposition: coordinating massive numbers of agents for real-world business applications at scale.