> ## Documentation Index
> Fetch the complete documentation index at: https://humandata.mangodesk.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Data

> Training data for autonomous agents and multi-step reasoning systems

## Supporting Autonomous Agent Development

Agent-based systems require specialized data to enable autonomous task execution. Unlike traditional conversational AI, agents must plan, execute, and adapt to achieve complex goals across extended workflows.

<Note>
  Agent training data focuses on decision-making, tool usage, and multi-step reasoning rather than just conversation.
</Note>

## Key Data Categories for Agents

<CardGroup cols={2}>
  <Card title="Planning Capabilities" icon="route">
    Teaching workflow decomposition, adaptive replanning, task delegation, and self-evaluation
  </Card>

  <Card title="Task Execution" icon="play">
    Improving specific skills like tool usage, code generation, and information synthesis
  </Card>

  <Card title="Extended Interactions" icon="clock">
    Training on lengthy exchanges to maintain context and coherence over time
  </Card>

  <Card title="User Preferences" icon="user-check">
    Collecting feedback on intermediate steps and final outputs
  </Card>
</CardGroup>

## Agent Training Data Types

### Task Decomposition Examples

<CodeGroup>
  ```json Project Management theme={null}
  {
    "task": "Create a monthly financial report",
    "plan": [
      {
        "step": 1,
        "action": "Gather financial data from all departments",
        "tools": ["database_query", "spreadsheet_reader"],
        "dependencies": [],
        "estimated_time": "30 minutes"
      },
      {
        "step": 2,
        "action": "Calculate key metrics (revenue, expenses, profit)",
        "tools": ["calculator", "data_analyzer"],
        "dependencies": [1],
        "estimated_time": "45 minutes"
      },
      {
        "step": 3,
        "action": "Create visualizations for trends",
        "tools": ["chart_generator"],
        "dependencies": [2],
        "estimated_time": "30 minutes"
      },
      {
        "step": 4,
        "action": "Write executive summary",
        "tools": ["text_generator"],
        "dependencies": [2, 3],
        "estimated_time": "20 minutes"
      }
    ],
    "evaluation_criteria": {
      "completeness": "All sections included",
      "accuracy": "Calculations verified",
      "clarity": "Easy to understand"
    }
  }
  ```

  ```json Research Task theme={null}
  {
    "task": "Analyze competitor landscape for SaaS product",
    "plan": [
      {
        "step": 1,
        "action": "Identify top 10 competitors",
        "tools": ["web_search", "industry_reports"],
        "dependencies": []
      },
      {
        "step": 2,
        "action": "Gather pricing and feature data",
        "tools": ["web_scraper", "api_calls"],
        "dependencies": [1]
      },
      {
        "step": 3,
        "action": "Analyze market positioning",
        "tools": ["data_analyzer", "visualization"],
        "dependencies": [2]
      },
      {
        "step": 4,
        "action": "Generate strategic recommendations",
        "tools": ["report_generator"],
        "dependencies": [3]
      }
    ]
  }
  ```
</CodeGroup>

### Tool Usage Training

<Tabs>
  <Tab title="API Integration">
    ```json theme={null}
    {
      "scenario": "Research competitor pricing",
      "actions": [
        {
          "thought": "I need to find current pricing for top 5 competitors",
          "action": "web_search",
          "action_input": "SaaS project management tool pricing 2024",
          "observation": "Found pricing pages for Asana, Monday, Trello..."
        },
        {
          "thought": "I should extract specific pricing tiers",
          "action": "web_scrape",
          "action_input": "https://asana.com/pricing",
          "observation": "Basic: $10.99/user, Premium: $24.99/user..."
        },
        {
          "thought": "Now I'll compile this into a comparison table",
          "action": "create_table",
          "action_input": {
            "headers": ["Tool", "Basic", "Premium", "Enterprise"],
            "data": [
              ["Asana", "$10.99", "$24.99", "Contact Sales"],
              ["Monday", "$8.00", "$16.00", "$24.00"]
            ]
          }
        }
      ]
    }
    ```
  </Tab>

  <Tab title="Code Generation">
    ```json theme={null}
    {
      "scenario": "Create data processing pipeline",
      "actions": [
        {
          "thought": "Need to process CSV files and generate reports",
          "action": "code_generate",
          "action_input": "Python script to read CSV, clean data, and create summary statistics",
          "observation": "Generated pandas-based script with error handling"
        },
        {
          "thought": "Should test the script with sample data",
          "action": "code_execute",
          "action_input": "test_script.py",
          "observation": "Script runs successfully, produces expected output"
        },
        {
          "thought": "Now I'll create documentation",
          "action": "documentation_generate",
          "action_input": "Document the data pipeline script with usage examples"
        }
      ]
    }
    ```
  </Tab>

  <Tab title="Database Operations">
    ```json theme={null}
    {
      "scenario": "Customer data analysis",
      "actions": [
        {
          "thought": "Need to query customer database for churn analysis",
          "action": "sql_query",
          "action_input": "SELECT customer_id, signup_date, last_login FROM customers WHERE last_login < '2024-01-01'",
          "observation": "Retrieved 1,247 potentially churned customers"
        },
        {
          "thought": "Should analyze patterns in the data",
          "action": "data_analysis",
          "action_input": "Analyze churn patterns by signup cohort and usage metrics",
          "observation": "Found 40% higher churn in Q3 2023 cohort"
        }
      ]
    }
    ```
  </Tab>
</Tabs>

### Error Recovery and Adaptation

<AccordionGroup>
  <Accordion title="API Failure Handling">
    ```json theme={null}
    {
      "scenario": "API call failure during data retrieval",
      "initial_action": {
        "type": "api_call",
        "endpoint": "/api/v1/users",
        "result": "error_timeout"
      },
      "recovery_steps": [
        {
          "analysis": "API timeout detected, might be temporary",
          "action": "retry_with_backoff",
          "parameters": {
            "max_retries": 3,
            "backoff_factor": 2
          }
        },
        {
          "analysis": "Still failing after retries",
          "action": "try_alternative_endpoint",
          "fallback": "/api/v2/users"
        },
        {
          "analysis": "No API access available",
          "action": "use_cached_data",
          "notification": "Using data from 2 hours ago",
          "impact_assessment": "Minimal - data freshness acceptable for task"
        }
      ]
    }
    ```
  </Accordion>

  <Accordion title="Plan Adaptation">
    ```json theme={null}
    {
      "scenario": "Meeting scheduling conflict",
      "original_plan": [
        {"step": 1, "action": "Schedule team meeting for 2 PM"},
        {"step": 2, "action": "Prepare presentation"},
        {"step": 3, "action": "Send meeting agenda"}
      ],
      "conflict_detected": {
        "issue": "Key participant unavailable at 2 PM",
        "constraint": "Must complete before end of week"
      },
      "revised_plan": [
        {"step": 1, "action": "Find alternative time slot"},
        {"step": 2, "action": "Poll team availability"},
        {"step": 3, "action": "Reschedule for 10 AM Friday"},
        {"step": 4, "action": "Adjust presentation timing"},
        {"step": 5, "action": "Send updated agenda"}
      ]
    }
    ```
  </Accordion>

  <Accordion title="Resource Constraint Handling">
    ```json theme={null}
    {
      "scenario": "Insufficient computational resources",
      "constraint": {
        "type": "memory_limit",
        "available": "8GB",
        "required": "16GB"
      },
      "adaptations": [
        {
          "strategy": "data_chunking",
          "description": "Process data in smaller batches",
          "trade_off": "Increased processing time"
        },
        {
          "strategy": "algorithm_substitution",
          "description": "Use memory-efficient algorithm variant",
          "trade_off": "Slightly reduced accuracy"
        },
        {
          "strategy": "cloud_scaling",
          "description": "Request additional compute resources",
          "cost_impact": "$50 estimated"
        }
      ]
    }
    ```
  </Accordion>
</AccordionGroup>

## Multi-Step Workflow Training

### Extended Task Examples

<Steps>
  <Step title="Content Creation Workflow">
    Complex multi-hour tasks requiring sustained attention:

    ```json theme={null}
    {
      "task": "Create comprehensive marketing campaign",
      "duration": "4-6 hours",
      "steps": [
        "Market research and competitor analysis",
        "Target audience definition",
        "Message and positioning development", 
        "Creative asset creation",
        "Campaign timeline planning",
        "Budget allocation",
        "Success metrics definition"
      ],
      "context_retention": "Must maintain brand voice throughout"
    }
    ```
  </Step>

  <Step title="Software Development Project">
    Multi-day development cycles:

    ```json theme={null}
    {
      "task": "Implement new API endpoint",
      "duration": "2-3 days",
      "phases": [
        "Requirements analysis",
        "API design and documentation",
        "Implementation",
        "Testing and debugging",
        "Code review feedback integration",
        "Deployment preparation"
      ],
      "dependencies": "Database schema changes, authentication updates"
    }
    ```
  </Step>

  <Step title="Data Analysis Project">
    Research and analysis workflows:

    ```json theme={null}
    {
      "task": "Customer behavior analysis",
      "duration": "1-2 weeks",
      "methodology": [
        "Data collection and validation",
        "Exploratory data analysis",
        "Hypothesis formation",
        "Statistical testing",
        "Insight generation",
        "Recommendation development",
        "Presentation creation"
      ],
      "deliverables": "Executive summary, detailed report, actionable recommendations"
    }
    ```
  </Step>
</Steps>

## Post-Deployment Data Collection

Agent systems generate valuable training data during real-world deployment:

<Tabs>
  <Tab title="Success Patterns">
    Collecting examples of effective agent behavior:

    ```json theme={null}
    {
      "task_id": "task_12345",
      "outcome": "successful",
      "metrics": {
        "completion_time": "23 minutes",
        "user_satisfaction": 4.8,
        "efficiency_score": 0.92
      },
      "learning_signals": [
        "Effective tool selection",
        "Optimal step ordering",
        "Good error recovery",
        "Clear communication"
      ]
    }
    ```
  </Tab>

  <Tab title="Failure Analysis">
    Learning from unsuccessful attempts:

    ```json theme={null}
    {
      "task_id": "task_67890",
      "outcome": "failed",
      "failure_point": "Step 3: Data analysis",
      "root_cause": "Insufficient context retention",
      "improvement_areas": [
        "Better working memory management",
        "More frequent context validation",
        "Enhanced error detection"
      ],
      "recovery_suggestions": [
        "Implement checkpoint system",
        "Add context summarization",
        "Improve error messaging"
      ]
    }
    ```
  </Tab>

  <Tab title="User Feedback">
    Direct feedback on agent performance:

    ```json theme={null}
    {
      "feedback_type": "intermediate_step",
      "step": "Data visualization creation",
      "user_rating": 3,
      "comments": "Chart is accurate but hard to read",
      "suggestions": [
        "Use different colors for better contrast",
        "Add data labels for clarity",
        "Increase font size"
      ],
      "agent_response": "Acknowledged, updating visualization parameters"
    }
    ```
  </Tab>
</Tabs>

## Agent Performance Metrics

<CardGroup cols={2}>
  <Card title="Task Success Rate" icon="trophy">
    **Target: >75%**

    * Completed objectives
    * Met user requirements
    * Achieved within time constraints
  </Card>

  <Card title="Efficiency Score" icon="gauge">
    **Target: `<1.5x` optimal**

    * Steps vs optimal path
    * Resource utilization
    * Time to completion
  </Card>

  <Card title="Error Recovery" icon="shield">
    **Target: >90%**

    * Successful failure handling
    * Graceful degradation
    * User communication
  </Card>

  <Card title="User Satisfaction" icon="heart">
    **Target: >80%**

    * Positive feedback
    * Task completion satisfaction
    * Would use again
  </Card>
</CardGroup>

## Critical Considerations for Agent Systems

<Warning>
  Given the compound nature of multi-step workflows, thorough evaluation at each stage becomes critical for system reliability.
</Warning>

### Evaluation Strategy

<Steps>
  <Step title="Component Testing">
    Test individual capabilities in isolation:

    * Tool usage accuracy
    * Planning logic quality
    * Error handling robustness
    * Context retention ability
  </Step>

  <Step title="Integration Testing">
    Verify component interactions:

    * Tool chaining effectiveness
    * State management consistency
    * Resource handling efficiency
    * Failure propagation control
  </Step>

  <Step title="End-to-End Validation">
    Test complete workflows:

    * Task completion rates
    * Time efficiency
    * Resource usage optimization
    * Output quality assessment
  </Step>

  <Step title="Human-in-the-Loop Testing">
    Validate with real users:

    * Usability studies
    * Preference collection
    * Failure analysis
    * Improvement suggestions
  </Step>
</Steps>

## Best Practices for Agent Data

<AccordionGroup>
  <Accordion title="Workflow Diversity">
    Ensure comprehensive scenario coverage:

    * Different task complexities
    * Various domain applications
    * Multiple user types
    * Edge cases and exceptions
    * Success and failure examples
  </Accordion>

  <Accordion title="Context Management">
    Train for long-term consistency:

    * Working memory updates
    * Goal tracking across sessions
    * State persistence
    * Context summarization
    * Priority management
  </Accordion>

  <Accordion title="Tool Integration">
    Comprehensive tool usage patterns:

    * Single tool mastery
    * Multi-tool workflows
    * Tool selection strategies
    * Error handling per tool
    * Performance optimization
  </Accordion>

  <Accordion title="Human Collaboration">
    Human-agent interaction patterns:

    * Clarification requests
    * Progress updates
    * Approval workflows
    * Feedback integration
    * Handoff procedures
  </Accordion>
</AccordionGroup>

## Continuous Learning Architecture

<Info>
  Agent systems benefit from continuous learning loops that incorporate real-world performance data back into training.
</Info>

### Feedback Integration Pipeline

<Tabs>
  <Tab title="Real-Time Learning">
    * Online adaptation to user preferences
    * Performance metric tracking
    * Error pattern detection
    * Success pattern reinforcement
  </Tab>

  <Tab title="Batch Updates">
    * Periodic model fine-tuning
    * Dataset expansion with new examples
    * Capability gap identification
    * Performance regression testing
  </Tab>

  <Tab title="Human Oversight">
    * Expert review of edge cases
    * Quality control processes
    * Safety validation
    * Ethical compliance checks
  </Tab>
</Tabs>