​
Output Format

The Company Research & Analysis Agent produces a comprehensive output that combines insights from multiple AI agents.

​
Output Schema

{
  "title": "Company Research Agent Output",
  "type": "object",
  "schemaVersion": 1,
  "properties": {
    "company_info": {
      "type": "object",
      "description": "Basic company information",
      "properties": {
        "name": "string",
        "domain": "string",
        "description": "string",
        "industry": "string",
        "founded_year": "integer",
        "headquarters": "string"
      }
    },
    "research_data": {
      "type": "object",
      "description": "Raw research data from various sources",
      "properties": {
        "linkedin_data": "object",
        "crunchbase_data": "object",
        "pitchbook_data": "object",
        "news_articles": "array"
      }
    },
    "analysis": {
      "type": "object",
      "description": "AI-generated analysis and insights",
      "properties": {
        "market_position": "string",
        "competitive_advantages": "array",
        "growth_metrics": "object",
        "risk_factors": "array"
      }
    },
    "report": {
      "type": "object",
      "description": "Structured report compiled by AI",
      "properties": {
        "executive_summary": "string",
        "detailed_sections": "array",
        "key_findings": "array",
        "recommendations": "array"
      }
    },
    "metadata": {
      "type": "object",
      "description": "Information about the research process",
      "properties": {
        "research_timestamp": "string",
        "data_sources": "array",
        "completion_status": "string"
      }
    }
  }
}

​
Output Components

​
1. Company Information

Basic company details gathered by the Research Specialist: 
{
  "company_info": {
    "name": "Apple Inc.",
    "domain": "apple.com",
    "description": "Technology company that designs, manufactures, and markets smartphones, computers, and consumer electronics",
    "industry": "Consumer Electronics",
    "founded_year": 1976,
    "headquarters": "Cupertino, California, United States"
  }
}

​
2. Research Data

Raw data collected from various sources: 
{
  "research_data": {
    "linkedin_data": {
      "employee_count": 164000,
      "followers": 28000000,
      "specialties": ["..."],
      "recent_updates": ["..."]
    },
    "crunchbase_data": {
      "funding_rounds": ["..."],
      "investors": ["..."],
      "acquisitions": ["..."]
    },
    "pitchbook_data": {
      "financials": {"..."},
      "market_share": {"..."},
      "competitors": ["..."]
    },
    "news_articles": [
      {
        "title": "...",
        "url": "...",
        "date": "...",
        "summary": "..."
      }
    ]
  }
}

​
3. Analysis

AI-generated insights from the Data Analyst: 
{
  "analysis": {
    "market_position": "Industry leader in consumer electronics and services",
    "competitive_advantages": [
      "Strong brand recognition",
      "Vertical integration",
      "Innovation capability"
    ],
    "growth_metrics": {
      "revenue_growth": "12%",
      "market_share_trend": "Increasing",
      "expansion_rate": "High"
    },
    "risk_factors": [
      "Supply chain dependencies",
      "Regulatory challenges",
      "Market saturation"
    ]
  }
}

​
4. Report

Structured report from the Content Compiler: 
{
  "report": {
    "executive_summary": "Comprehensive overview of company status...",
    "detailed_sections": [
      {
        "title": "Business Overview",
        "content": "..."
      },
      {
        "title": "Market Analysis",
        "content": "..."
      }
    ],
    "key_findings": [
      "Strong market position in premium segment",
      "Diversifying revenue streams",
      "Expanding services portfolio"
    ],
    "recommendations": [
      "Monitor emerging competitors",
      "Focus on service expansion",
      "Strengthen supply chain"
    ]
  }
}

​
5. Metadata

Information about the research process: 
{
  "metadata": {
    "research_timestamp": "2024-01-20T10:30:00Z",
    "data_sources": [
      "LinkedIn",
      "Crunchbase",
      "PitchBook",
      "Google News"
    ],
    "completion_status": "success"
  }
}

​
Output Processing

The output goes through several stages:
  1. Data Collection
    • Raw data gathered by Research Specialist
    • Multiple sources accessed in parallel
  2. Analysis
    • Data Analyst processes raw information
    • Pattern recognition and insight extraction
  3. Report Generation
    • Content Compiler structures findings
    • Formats for readability and clarity

​
Error Handling

The output includes error information when applicable: 
{
  "error": {
    "code": "DATA_SOURCE_ERROR",
    "message": "Unable to access LinkedIn data",
    "source": "linkedin_scraper",
    "timestamp": "2024-01-20T10:30:00Z"
  }
}
The output format is designed to be both machine-readable and human-friendly. The structured data can be used programmatically, while the report sections provide narrative insights.