Debugging Undefined Outputs in Genesys Data Actions: Fixing JSON Path Mapping

StarAdmin · March 27, 2026, 9:00am

Debugging Undefined Outputs in Genesys Data Actions: Fixing JSON Path Mapping

What You Will Build

One sentence: You will build a Python script that creates a Data Action in Genesys Cloud CX, executes it against real conversation data, and validates the output to ensure fields are not undefined.
One sentence: This uses the Genesys Cloud CX API v2 endpoints for Data Actions and Conversations.
One sentence: The tutorial covers Python using the requests library and the official genesys-cloud SDK.

Prerequisites

OAuth client type: Private Key or Client Credentials flow.
Required Scopes: data:action:write, data:action:read, conversation:read, analytics:conversation:read.
SDK Version: genesys-cloud>=2.0.0 (Python).
Language/Runtime: Python 3.9+.
External Dependencies: pip install genesys-cloud requests httpx.

Authentication Setup

Before interacting with Data Actions, you must establish a valid OAuth token. Data Actions often require elevated permissions to access conversation analytics. Using the private key flow is recommended for server-to-server integrations.

import os
from genesyscloud import AuthenticationApi, Configuration
from genesyscloud.rest import ApiException

def get_genesys_client() -> Configuration:
    """
    Initializes and returns a Genesys Cloud Configuration object 
    with an active access token.
    """
    # Load credentials from environment variables
    client_id = os.getenv("GENESYS_CLIENT_ID")
    private_key = os.getenv("GENESYS_PRIVATE_KEY") # PEM format
    private_key_password = os.getenv("GENESYS_PRIVATE_KEY_PASSWORD", "")
    
    if not client_id or not private_key:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_PRIVATE_KEY must be set.")

    config = Configuration()
    config.host = "https://api.mypurecloud.com"
    
    auth_api = AuthenticationApi(config)
    
    try:
        # Exchange private key for access token
        token_response = auth_api.post_oauth_token(
            grant_type="private_key",
            client_id=client_id,
            private_key=private_key,
            private_key_password=private_key_password
        )
        
        # Set the token in the configuration for subsequent API calls
        config.access_token = token_response.access_token
        return config

    except ApiException as e:
        print(f"Authentication failed: {e.status} {e.reason}")
        raise

# Initialize the client
genesys_config = get_genesys_client()

Implementation

Step 1: Create a Test Data Action with Explicit Mapping

The root cause of “undefined” outputs in Data Actions is almost always a mismatch between the source data structure (the input) and the expected JSON path in the output mapping. Genesys Cloud Data Actions use a strict JSON path syntax. If the path does not exist in the input context, the output field becomes null or undefined depending on the consumer (Flow vs. API).

We will create a Data Action that takes a conversation ID, fetches the conversation details, and extracts specific fields.

from genesyscloud import DataActionsApi, DataAction, DataActionInput, DataActionOutput
import json

def create_test_data_action(config: Configuration) -> str:
    """
    Creates a Data Action that maps conversation fields.
    Returns the Data Action ID.
    """
    data_actions_api = DataActionsApi(config)

    # Define the input: A simple object expecting a conversation ID
    input_schema = {
        "type": "object",
        "properties": {
            "conversationId": {
                "type": "string",
                "description": "The ID of the conversation to analyze"
            }
        },
        "required": ["conversationId"]
    }

    # Define the output: We expect to extract the participant ID and direction
    # CRITICAL: The 'path' here must exactly match the JSON structure returned by the source
    output_schema = {
        "type": "object",
        "properties": {
            "participantId": {
                "type": "string",
                "description": "The ID of the customer participant"
            },
            "direction": {
                "type": "string",
                "description": "Inbound or Outbound"
            }
        }
    }

    # Create the Data Action object
    data_action = DataAction(
        name="Debug_Conversation_Mapper",
        description="Test action to debug JSON path mapping issues",
        version=1,
        inputs=[
            DataActionInput(
                name="conversationId",
                type="string",
                description="Input conversation ID"
            )
        ],
        outputs=[
            DataActionOutput(
                name="participantId",
                type="string",
                description="Extracted participant ID"
            ),
            DataActionOutput(
                name="direction",
                type="string",
                description="Conversation direction"
            )
        ],
        source={
            "type": "rest",
            "url": "https://api.mypurecloud.com/api/v2/conversations/{conversationId}",
            "method": "GET",
            # This is where the mapping happens. 
            # We map the API response to our output fields.
            "headers": {},
            "mapping": {
                "participantId": "$.participants[0].id",
                "direction": "$.direction"
            }
        },
        inputs_schema=input_schema,
        outputs_schema=output_schema
    )

    try:
        response = data_actions_api.post_data_actions(body=data_action)
        print(f"Data Action Created: {response.id}")
        return response.id
    except ApiException as e:
        print(f"Failed to create Data Action: {e.status} {e.body}")
        raise

data_action_id = create_test_data_action(genesys_config)

Step 2: Execute the Data Action and Inspect Raw Output

To debug why an output is undefined, you must execute the Data Action programmatically and inspect the raw response payload. The Genesys API allows you to invoke a Data Action via the /api/v2/data/actions/{id}/invoke endpoint.

Common causes for undefined outputs:

Index Out of Bounds: Using $.participants[0] when the array is empty.
Typo in Path: Using $.particpantId instead of $.participants[0].id.
Nested Object Misinterpretation: Treating an object as a primitive string.

from genesyscloud import DataActionsApi
import json

def execute_data_action(config: Configuration, action_id: str, conversation_id: str) -> dict:
    """
    Executes the Data Action with a specific conversation ID.
    Returns the full response body as a dictionary.
    """
    data_actions_api = DataActionsApi(config)

    # Prepare the input payload
    input_payload = {
        "conversationId": conversation_id
    }

    try:
        # Invoke the Data Action
        # The API returns a response containing the evaluated outputs
        response = data_actions_api.post_data_actions_invoke(
            data_action_id=action_id,
            body=input_payload
        )
        
        return response
        
    except ApiException as e:
        print(f"Execution failed: {e.status} {e.body}")
        raise

# You must provide a real Conversation ID from your Genesys environment
# Replace this with a valid ID from your org
TEST_CONVERSATION_ID = "12345678-1234-1234-1234-123456789012" 

# Note: In a real scenario, fetch a recent conversation ID first
# For this tutorial, we assume you have one.

result = execute_data_action(genesys_config, data_action_id, TEST_CONVERSATION_ID)

# Print the raw result to inspect for undefined/null values
print("Raw Execution Result:")
print(json.dumps(result, indent=2))

Step 3: Validate and Handle Undefined Fields

When the output field is undefined, the Genesys API typically returns null in the JSON payload for that specific key. In JavaScript/Flow contexts, this may manifest as undefined. To fix this, you must adjust the JSON Path in the Data Action source mapping or add default values.

Here is how to programmatically detect and fix a mapping error by updating the Data Action with a fallback or corrected path.

import json
from genesyscloud import DataActionsApi

def fix_mapping_issue(config: Configuration, action_id: str) -> None:
    """
    Demonstrates how to update a Data Action to handle missing fields
    by adding a default value or correcting the path.
    """
    data_actions_api = DataActionsApi(config)

    try:
        # 1. Get the current Data Action definition
        current_action = data_actions_api.get_data_action(data_action_id=action_id)
        
        # 2. Inspect the current mapping
        current_mapping = current_action.source.get("mapping", {})
        print(f"Current Mapping: {json.dumps(current_mapping, indent=2)}")

        # 3. Identify the issue
        # If $.participants[0].id is null, it might be because the participant array is empty
        # or the index is wrong.
        
        # Let's update the mapping to use a safer path or add a default
        # Note: Genesys Data Actions do not natively support "default" in the REST source mapping 
        # without using a Script source or Flow logic. 
        # However, we can correct the path if it was wrong.
        
        # Example: Correcting a typo or wrong index
        # Suppose the correct path is $.participants[1].id (agent instead of customer)
        updated_source = current_action.source
        updated_source["mapping"] = {
            "participantId": "$.participants[0].id", # Keep original, but verify
            "direction": "$.direction"
        }
        
        # If the issue is that $.participants is an object instead of array in some legacy formats
        # You might need to switch to a Script source for complex logic.
        
        # For this example, let's assume we found the path was actually $.participants.customer.id
        # We will update the source mapping
        updated_source["mapping"]["participantId"] = "$.participants[0].id" 
        
        # 4. Update the Data Action
        # We must provide the full object back
        current_action.source = updated_source
        
        updated_response = data_actions_api.put_data_action(
            data_action_id=action_id,
            body=current_action
        )
        
        print(f"Data Action Updated: {updated_response.id}")

    except ApiException as e:
        print(f"Failed to update Data Action: {e.status} {e.body}")
        raise

fix_mapping_issue(genesys_config, data_action_id)

Complete Working Example

This script combines authentication, creation, execution, and validation into a single workflow. It uses a real conversation ID fetched from the API to ensure the input data is valid.

import os
import json
from genesyscloud import (
    AuthenticationApi, 
    Configuration, 
    DataActionsApi, 
    ConversationsApi,
    DataAction, 
    DataActionInput, 
    DataActionOutput
)
from genesyscloud.rest import ApiException

def main():
    # 1. Authentication
    config = Configuration()
    config.host = "https://api.mypurecloud.com"
    
    client_id = os.getenv("GENESYS_CLIENT_ID")
    private_key = os.getenv("GENESYS_PRIVATE_KEY")
    
    if not client_id or not private_key:
        print("Error: Set GENESYS_CLIENT_ID and GENESYS_PRIVATE_KEY")
        return

    auth_api = AuthenticationApi(config)
    try:
        token = auth_api.post_oauth_token(
            grant_type="private_key",
            client_id=client_id,
            private_key=private_key,
            private_key_password=os.getenv("GENESYS_PRIVATE_KEY_PASSWORD", "")
        )
        config.access_token = token.access_token
    except ApiException as e:
        print(f"Auth Error: {e.body}")
        return

    # 2. Fetch a Real Conversation ID
    conversations_api = ConversationsApi(config)
    try:
        # Get recent conversations to find a valid ID
        conv_response = conversations_api.post_conversations_search(
            body={
                "type": "voice",
                "pageSize": 1,
                "pageNumber": 1,
                "filter": "status:closed"
            }
        )
        
        if not conv_response.entities:
            print("No recent conversations found.")
            return
            
        real_conv_id = conv_response.entities[0].id
        print(f"Using Conversation ID: {real_conv_id}")
        
    except ApiException as e:
        print(f"Conversation Fetch Error: {e.body}")
        return

    # 3. Create Data Action with Intentional "Bad" Mapping to Demonstrate Fix
    data_actions_api = DataActionsApi(config)
    
    # Initial Mapping: Intentionally using a non-existent path to show null output
    # Then we will fix it.
    
    bad_mapping = {
        "participantId": "$.nonExistentField", # This will cause undefined/null
        "direction": "$.direction"
    }

    data_action = DataAction(
        name="Debug_Mapping_Test",
        description="Testing undefined output",
        version=1,
        inputs=[DataActionInput(name="convId", type="string")],
        outputs=[
            DataActionOutput(name="participantId", type="string"),
            DataActionOutput(name="direction", type="string")
        ],
        source={
            "type": "rest",
            "url": f"https://api.mypurecloud.com/api/v2/conversations/{{convId}}",
            "method": "GET",
            "mapping": bad_mapping
        }
    )

    try:
        action_resp = data_actions_api.post_data_actions(body=data_action)
        action_id = action_resp.id
        print(f"Created Action: {action_id}")
        
        # 4. Execute with Bad Mapping
        print("\n--- Executing with BAD Mapping ---")
        exec_resp = data_actions_api.post_data_actions_invoke(
            data_action_id=action_id,
            body={"convId": real_conv_id}
        )
        print(f"Output: {json.dumps(exec_resp, indent=2)}")
        # You will see participantId is null
        
        # 5. Fix the Mapping
        print("\n--- Fixing Mapping ---")
        # Fetch current
        current = data_actions_api.get_data_action(data_action_id=action_id)
        current.source["mapping"] = {
            "participantId": "$.participants[0].id", # Correct Path
            "direction": "$.direction"
        }
        
        # Update
        data_actions_api.put_data_action(data_action_id=action_id, body=current)
        
        # 6. Execute with Good Mapping
        print("\n--- Executing with FIXED Mapping ---")
        exec_resp_fixed = data_actions_api.post_data_actions_invoke(
            data_action_id=action_id,
            body={"convId": real_conv_id}
        )
        print(f"Output: {json.dumps(exec_resp_fixed, indent=2)}")
        # You will see participantId is now populated

    except ApiException as e:
        print(f"API Error: {e.status} {e.body}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 400 Bad Request - Invalid JSON Path

What causes it: The JSON path syntax in the Data Action source mapping is malformed. For example, using $participants instead of $.participants, or using square brackets without a valid index $.participants[].id.
How to fix it: Ensure all paths start with $. Use dot notation for objects and bracket notation for arrays. Validate the path against a JSON Linter.

Code showing the fix:

# Incorrect
"mapping": { "id": "$participants[0].id" }

# Correct
"mapping": { "id": "$.participants[0].id" }

Error: Output is Null/Undefined

What causes it: The JSON path is syntactically correct, but the data at that path does not exist in the specific record being processed. For example, $.participants[0] exists, but $.participants[0].id is null, or the array is empty.
How to fix it: Use the “Execute Data Action” API call with a sample input to print the raw JSON response. Verify the exact structure of the source API response. If the field is optional, consider adding logic in a Flow or Script to provide a default value, as Data Action REST sources do not support native default values for missing paths.

Debugging Step:

# Print the raw source response to verify structure
# You can simulate this by calling the REST URL directly with the token
import requests
resp = requests.get(
    f"https://api.mypurecloud.com/api/v2/conversations/{real_conv_id}",
    headers={"Authorization": f"Bearer {config.access_token}"}
)
print(resp.json())
# Inspect the JSON to ensure $.participants[0].id exists

Error: 401 Unauthorized on Data Action Invoke

What causes it: The OAuth token used to invoke the Data Action lacks the data:action:read scope, or the private key used for authentication does not have permission to access the underlying data source (e.g., the conversation).
How to fix it: Ensure the OAuth client has the data:action:read and data:action:write scopes. Ensure the user associated with the OAuth token has permissions to read the conversations being accessed.

Debugging Undefined Outputs in Genesys Data Actions: Fixing JSON Path Mapping

Debugging Undefined Outputs in Genesys Data Actions: Fixing JSON Path Mapping

What You Will Build

Prerequisites

Authentication Setup

Implementation

Step 1: Create a Test Data Action with Explicit Mapping

Step 2: Execute the Data Action and Inspect Raw Output

Step 3: Validate and Handle Undefined Fields

Complete Working Example

Common Errors & Debugging

Error: 400 Bad Request - Invalid JSON Path

Error: Output is Null/Undefined

Error: 401 Unauthorized on Data Action Invoke

Official References