RPA Automation Management

Identify the automation need and provide specific guidance:

Python
# Example: Web scraping automation
{
  "task_type": "web_automation",
  "actions": [
    {"type": "navigate", "url": "https://example.com"},
    {"type": "wait_for_element", "selector": ".data-table"},
    {"type": "extract_data", "selector": "tr td", "attribute": "text"},
    {"type": "save_to_file", "format": "csv", "filename": "scraped_data.csv"}
  ]
}

1. Request Analysis

   • Parse automation requirements
   • Identify complexity level (simple task vs complex workflow)
   • Determine resource needs (web actions, file operations, API calls)

2. Solution Design

   Progress:
   - [ ] Map user requirements to automation components
   - [ ] Define task sequence or workflow structure
   - [ ] Specify required actions and parameters
   - [ ] Add error handling and validation
   

3. Implementation Guidance

   • Provide JSON task/workflow structure
   • Include specific action parameters
   • Add configuration details
   • Suggest testing approach

Example 1: Simple Web Form Automation Input: "Automate filling out a contact form on a website" Output:

JSON
{
  "name": "contact_form_automation",
  "type": "linear_task",
  "actions": [
    {"type": "navigate", "url": "https://site.com/contact"},
    {"type": "fill_field", "selector": "#name", "value": "{{user_name}}"},
    {"type": "fill_field", "selector": "#email", "value": "{{user_email}}"},
    {"type": "fill_field", "selector": "#message", "value": "{{message_text}}"},
    {"type": "click", "selector": "#submit_btn"},
    {"type": "wait_for_element", "selector": ".success_message"}
  ]
}

Example 2: Complex Data Processing Workflow Input: "Process CSV files, validate data, and send different emails based on validation results" Output:

JSON
{
  "name": "data_validation_workflow",
  "type": "conditional_workflow",
  "nodes": [
    {"id": "read_csv", "type": "file_operation", "action": "read_csv"},
    {"id": "validate", "type": "data_validation", "rules": ["email_format", "required_fields"]},
    {"id": "branch", "type": "conditional", "condition": "validation_passed"},
    {"id": "success_email", "type": "email", "template": "success_template"},
    {"id": "error_email", "type": "email", "template": "error_template"}
  ],
  "edges": [
    {"from": "read_csv", "to": "validate"},
    {"from": "validate", "to": "branch"},
    {"from": "branch", "to": "success_email", "condition": "true"},
    {"from": "branch", "to": "error_email", "condition": "false"}
  ]
}

Example 3: System Configuration Input: "Set up MCP server for database connections" Output:

JSON
{
  "mcp_config": {
    "server_name": "database_connector",
    "connection_string": "postgresql://user:pass@localhost:5432/dbname",
    "timeout": 30,
    "retry_attempts": 3,
    "tools": ["query", "insert", "update", "delete"]
  }
}

Linear Tasks (Single Sequence)

• Web scraping and data extraction
• Form filling and submission
• File processing operations
• API calls and data synchronization

Workflow Orchestration (Multi-Node)

• Conditional branching based on data
• Parallel processing of multiple inputs
• Error handling and retry mechanisms
• Integration of multiple automation tasks

System Management

• MCP server configuration
• Task scheduling and triggers
• Performance monitoring
• Security and access control

• Use descriptive names for tasks and workflows
• Include error handling for network operations
• Add wait conditions for dynamic content
• Implement retry logic for unreliable operations
• Use variables for reusable values
• Test with sample data before production

• Don't hardcode values that should be variables
• Don't skip wait conditions for dynamic elements
• Don't create overly complex workflows without breaking them down
• Don't forget to handle edge cases and errors
• Don't mix task-level and workflow-level operations