Data Flow Architecture

Summary

This document maps out data transformations across research ingestion, content drafting, safety validation, publishing, and analytical feedback loops.

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1. Primary Content Data Pipeline

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2. Data Structures & Transformation Maps

2.1 Research Ingestion Payload

{
  "notebook_id": "d4bf85cb-37b1-4ec9-8405-f63d99bee7f4",
  "topic": "AI Agents 2026",
  "insights": [
    "Autonomous agent orchestration reduces manual task overhead by 80%.",
    "Model Context Protocol (MCP) bridges tools seamlessly."
  ]
}

2.2 Post Output Object

{
  "topic": "AI Agents 2026",
  "post_text": "🚀 AI Agents in 2026 are transforming how teams build software...\n\n#AIAgents #Automation",
  "validation": {
    "length_check": true,
    "soul_tone_check": true,
    "hashtag_check": true
  },
  "dry_run": false,
  "post_url": "https://www.linkedin.com/feed/update/urn:li:share:123456789"
}

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3. Related Documentation

• System Architecture
• Database & State Persistence
• API Reference