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Overview

Coder Tasks allow you to execute pre-defined code artifacts within your workflow. These tasks integrate the computational power of Coder Artifacts into your workflow processes. Coder Tasks are ideal for custom calculations, data transformations, and complex business logic implementation.

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Visual Example

Coder Task Example

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Configuration Structure

{
  "type": "CODER",
  "block": {
    "name": "Coder Task Name",
    "type": "CODER",
    "instructions": "Task instructions",
    "code_artifact_id": 123,
    "input_parameters": [],
    "expected_output": [],
    "dependencies": [],
    "error_policy": "RAISE"
  }
}

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Required Fields

FieldTypeDescriptionRequired
namestringTask identifier✅
instructionsstringTask instructions✅
code_artifact_idintegerReference to code artifact✅
input_parametersarrayInput configuration✅
expected_outputarrayOutput configuration✅
dependenciesarrayTask dependencies❌
error_policystringError handling strategy❌

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Input Parameters

The input parameters define what data your Coder Task will receive. These parameters will be passed to the arguments dictionary in your Coder Artifact function.

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Basic Input

{
  "name": "data",
  "type": "STRING",
  "description": "Input data for processing",
  "required": true,
  "source": "task_config"
}

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Complex Input

{
  "name": "config",
  "type": "OBJECT",
  "description": "Configuration object",
  "properties": [
    {
      "name": "algorithm",
      "type": "STRING",
      "description": "Algorithm selection",
      "required": true
    },
    {
      "name": "parameters",
      "type": "OBJECT",
      "description": "Algorithm parameters",
      "properties": [
        {
          "name": "iterations",
          "type": "INTEGER",
          "description": "Number of iterations",
          "required": true
        }
      ]
    }
  ]
}

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Output Parameters

Output parameters define the structure of data that your Coder Task will return. These should match the structure returned by your Coder Artifact function.

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Simple Output

{
  "name": "result",
  "type": "STRING",
  "description": "Processing result"
}

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Complex Output

{
  "name": "analysis_result",
  "type": "OBJECT",
  "properties": {
    "result": {
      "type": "FLOAT",
      "description": "Calculated value"
    },
    "metadata": {
      "type": "OBJECT",
      "description": "Processing metadata"
    },
    "logs": {
      "type": "ARRAY",
      "items": {
        "type": "STRING"
      },
      "description": "Processing logs"
    }
  }
}

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Workflow Integration

Coder Tasks act as computational nodes in your workflow, taking input from previous tasks, processing it using the referenced Coder Artifact, and outputting results for subsequent tasks.

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Common Use Cases

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1. Data Transformation

{
  "name": "Transform Data",
  "code_artifact_id": 123,
  "instructions": "Transform input data according to specified rules",
  "input_parameters": [
    {
      "name": "data",
      "type": "OBJECT",
      "description": "Raw data object",
      "required": true
    },
    {
      "name": "transformation_rules",
      "type": "ARRAY",
      "description": "Transformation rules",
      "required": true
    }
  ],
  "expected_output": [
    {
      "name": "transformed_data",
      "type": "OBJECT",
      "description": "Transformed data"
    }
  ]
}

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2. Custom Calculation

{
  "name": "Calculate Metrics",
  "code_artifact_id": 456,
  "instructions": "Calculate custom business metrics",
  "input_parameters": [
    {
      "name": "raw_metrics",
      "type": "ARRAY",
      "description": "Raw metric data",
      "required": true
    }
  ],
  "expected_output": [
    {
      "name": "calculated_metrics",
      "type": "OBJECT",
      "properties": {
        "kpi1": "FLOAT",
        "kpi2": "FLOAT",
        "summary": "STRING"
      }
    }
  ]
}

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3. Invoice Processing

{
  "name": "Calculate Invoice Information",
  "code_artifact_id": 789,
  "instructions": "Compute the total invoice amount based on the collected item details",
  "input_parameters": [
    {
      "name": "items_to_be_billed",
      "type": "ARRAY",
      "items": {
        "type": "OBJECT",
        "properties": {
          "item_price": { "type": "STRING" },
          "item_quantity": { "type": "STRING" }
        }
      },
      "description": "List of items with price and quantity",
      "required": true
    }
  ],
  "expected_output": [
    {
      "name": "calculation_amount",
      "type": "OBJECT",
      "properties": {
        "total_tax": { "type": "STRING" },
        "current_date": { "type": "STRING" },
        "total_amount": { "type": "STRING" },
        "total_state_tax": { "type": "STRING" },
        "total_central_tax": { "type": "STRING" },
        "total_amount_with_gst": { "type": "STRING" }
      },
      "description": "Calculated invoice amounts"
    }
  ]
}

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Best Practices

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1. Code Artifact Management

✅ Do:
  • Use version control for your Coder Artifacts
  • Document dependencies clearly
  • Include test cases with your Coder Artifacts
  • Handle errors gracefully in your Python code (as shown in the Coder Artifact documentation)
❌ Don’t:
  • Hard-code configurations that might change
  • Ignore error cases in your Coder Artifact code
  • Skip input validation in your Python function
  • Mix business logic between Coder Tasks and Coder Artifacts

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2. Input Parameters

✅ Do:
  • Validate all inputs in your Coder Artifact code
  • Document input requirements thoroughly
  • Use appropriate types that match your Coder Artifact expectations
  • Include examples in your documentation
❌ Don’t:
  • Skip validation in your Coder Artifact code
  • Use ambiguous parameter names
  • Ignore data types that will cause conversion issues

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3. Output Handling

✅ Do:
  • Define clear output structure that matches your Coder Artifact return values
  • Include error information in your response
  • Add processing metadata when useful
  • Validate outputs in your Coder Artifact code
❌ Don’t:
  • Return raw errors without context
  • Skip validation of output data
  • Use inconsistent formats between Coder Artifact and Coder Task configurations

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⚠️ Error Handling

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Configuration

{
  "error_policy": "RAISE",
  "retry_count": 3,
  "retry_delay": 60
}

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Error Types

  1. Input Validation Errors: Occurs when input doesn’t match expected format
  2. Processing Errors: Occurs during execution of the Coder Artifact
  3. System Errors: Occurs due to system limitations or failures

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🔗 Integration Examples

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With AI Task

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With Data Processing

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❓ Common Issues and Solutions

IssueSolution
Code Artifact Not FoundVerify artifact ID and ensure it’s published
Input Validation FailedCheck input types and format against Coder Artifact requirements
Processing ErrorReview error logs in your Coder Artifact and retry
Memory IssuesOptimize data handling in your Python code

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🏢 Examples by Industry

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Finance

{
  "name": "Risk Calculator",
  "code_artifact_id": 789,
  "instructions": "Calculate investment risk metrics",
  "input_parameters": [
    {
      "name": "portfolio_data",
      "type": "OBJECT",
      "description": "Portfolio information",
      "required": true
    }
  ],
  "expected_output": [
    {
      "name": "risk_metrics",
      "type": "OBJECT",
      "properties": {
        "var": "FLOAT",
        "sharpe_ratio": "FLOAT",
        "risk_level": "STRING"
      }
    }
  ]
}

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🔄 Connection with Coder Artifacts

Coder Tasks rely on well-designed Coder Artifacts to function properly. When creating a Coder Task:
  1. Select an existing Coder Artifact from your knowledge base
  2. Configure input parameters that match the expectations of your Coder Artifact’s function
  3. Define expected output that aligns with what your Coder Artifact function returns
  4. Provide clear instructions for maintainers to understand the task’s purpose
For detailed information on creating effective Coder Artifacts, refer to the Coder Artifact documentation.