Agent Data

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.

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

Key Data Categories for Agents

Planning Capabilities

Teaching workflow decomposition, adaptive replanning, task delegation, and self-evaluation

Task Execution

Improving specific skills like tool usage, code generation, and information synthesis

Extended Interactions

Training on lengthy exchanges to maintain context and coherence over time

User Preferences

Collecting feedback on intermediate steps and final outputs

Agent Training Data Types

Task Decomposition Examples

{
  "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"
  }
}

Tool Usage Training

{
  "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"]
        ]
      }
    }
  ]
}

Error Recovery and Adaptation

API Failure Handling

{
  "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"
    }
  ]
}

Plan Adaptation

{
  "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"}
  ]
}

Resource Constraint Handling

{
  "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"
    }
  ]
}

Multi-Step Workflow Training

Extended Task Examples

Content Creation Workflow

Complex multi-hour tasks requiring sustained attention:

{
  "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"
}

Software Development Project

Multi-day development cycles:

{
  "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"
}

Data Analysis Project

Research and analysis workflows:

{
  "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"
}

Post-Deployment Data Collection

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

Collecting examples of effective agent behavior:

{
  "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"
  ]
}

Agent Performance Metrics

Task Success Rate

Target: >75%

Completed objectives
Met user requirements
Achieved within time constraints

Efficiency Score

Target: <1.5x optimal

Steps vs optimal path
Resource utilization
Time to completion

Error Recovery

Target: >90%

Successful failure handling
Graceful degradation
User communication

User Satisfaction

Target: >80%

Positive feedback
Task completion satisfaction
Would use again

Critical Considerations for Agent Systems

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

Evaluation Strategy

Component Testing

Test individual capabilities in isolation:

Tool usage accuracy
Planning logic quality
Error handling robustness
Context retention ability

Integration Testing

Verify component interactions:

Tool chaining effectiveness
State management consistency
Resource handling efficiency
Failure propagation control

End-to-End Validation

Test complete workflows:

Task completion rates
Time efficiency
Resource usage optimization
Output quality assessment

Human-in-the-Loop Testing

Validate with real users:

Usability studies
Preference collection
Failure analysis
Improvement suggestions

Best Practices for Agent Data

Workflow Diversity

Context Management

Tool Integration

Human Collaboration

Continuous Learning Architecture

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

Feedback Integration Pipeline

Online adaptation to user preferences
Performance metric tracking
Error pattern detection
Success pattern reinforcement

Work with MangoDesk

Types of data

Supporting Autonomous Agent Development

Key Data Categories for Agents

Planning Capabilities

Task Execution

Extended Interactions

User Preferences

Agent Training Data Types

Task Decomposition Examples

Tool Usage Training

Error Recovery and Adaptation

Multi-Step Workflow Training

Extended Task Examples

Post-Deployment Data Collection

Agent Performance Metrics

Task Success Rate

Efficiency Score

Error Recovery

User Satisfaction

Critical Considerations for Agent Systems

Evaluation Strategy

Best Practices for Agent Data

Continuous Learning Architecture

Feedback Integration Pipeline

Work with MangoDesk

Types of data

​Supporting Autonomous Agent Development

​Key Data Categories for Agents

Planning Capabilities

Task Execution

Extended Interactions

User Preferences

​Agent Training Data Types

​Task Decomposition Examples

​Tool Usage Training

​Error Recovery and Adaptation

​Multi-Step Workflow Training

​Extended Task Examples

​Post-Deployment Data Collection

​Agent Performance Metrics

Task Success Rate

Efficiency Score

Error Recovery

User Satisfaction

​Critical Considerations for Agent Systems

​Evaluation Strategy

​Best Practices for Agent Data

​Continuous Learning Architecture

​Feedback Integration Pipeline

Supporting Autonomous Agent Development

Key Data Categories for Agents

Agent Training Data Types

Task Decomposition Examples

Tool Usage Training

Error Recovery and Adaptation

Multi-Step Workflow Training

Extended Task Examples

Post-Deployment Data Collection

Agent Performance Metrics

Critical Considerations for Agent Systems

Evaluation Strategy

Best Practices for Agent Data

Continuous Learning Architecture

Feedback Integration Pipeline