Invoking AWS Lambda from Genesys Cloud Architect via Data Actions

What You Will Build

• One sentence: You will build a Genesys Cloud Architect flow that triggers an AWS Lambda function using the aws:execute data action.
• One sentence: This uses the Genesys Cloud Architect API for flow definition and AWS IAM APIs for role configuration.
• One sentence: The programming languages covered are Python (for AWS IAM setup) and JSON/JavaScript (for Architect flow configuration).

Prerequisites

• AWS IAM Permissions: iam:CreateRole, iam:AttachRolePolicy, iam:PutRolePolicy, iam:GetRole.
• Genesys Cloud Permissions: flow:create, flow:view, flow:edit.
• SDK/API Versions: Genesys Cloud v2 API, AWS SDK for Python (Boto3) v1.
• External Dependencies: boto3 (Python), requests (Python).

Authentication Setup

AWS Authentication

You must configure AWS credentials for the Boto3 client. The recommended method is using environment variables or a shared credentials file.

import os
import boto3

# Ensure these environment variables are set
# AWS_ACCESS_KEY_ID
# AWS_SECRET_ACCESS_KEY
# AWS_DEFAULT_REGION

iam_client = boto3.client('iam', region_name=os.environ.get('AWS_DEFAULT_REGION', 'us-east-1'))

Genesys Cloud Authentication

You must obtain an OAuth 2.0 client credentials token. This tutorial assumes you have a Genesys Cloud API key (Client ID and Secret) with the flow:create scope.

import requests
import base64

CLIENT_ID = os.environ['GENESYS_CLIENT_ID']
CLIENT_SECRET = os.environ['GENESYS_CLIENT_SECRET']
ENVIRONMENT = os.environ['GENESYS_ENVIRONMENT']  # e.g., 'mypurecloud.com'

def get_genesys_token():
    """
    Retrieves an OAuth 2.0 client credentials token from Genesys Cloud.
    """
    auth_url = f"https://api.{ENVIRONMENT}/oauth/token"
    
    # Base64 encode the client_id:client_secret
    credentials = f"{CLIENT_ID}:{CLIENT_SECRET}"
    encoded_credentials = base64.b64encode(credentials.encode('utf-8')).decode('utf-8')
    
    headers = {
        'Authorization': f'Basic {encoded_credentials}',
        'Content-Type': 'application/x-www-form-urlencoded'
    }
    
    data = {
        'grant_type': 'client_credentials',
        'scope': 'flow:create flow:view flow:edit'
    }
    
    response = requests.post(auth_url, headers=headers, data=data)
    
    if response.status_code != 200:
        raise Exception(f"Failed to get Genesys token: {response.text}")
        
    return response.json()['access_token']

genesys_token = get_genesys_token()
genesys_headers = {
    'Authorization': f'Bearer {genesys_token}',
    'Content-Type': 'application/json'
}

Implementation

Step 1: Create the AWS IAM Role for Lambda Invocation

Genesys Cloud does not directly authenticate with AWS. Instead, the aws:execute data action relies on pre-configured AWS credentials stored in Genesys Cloud. However, the critical security boundary is the IAM Role that allows the AWS service (or the user context) to invoke the Lambda function.

In this pattern, we assume you are configuring an IAM Role that will be associated with an AWS credential set in Genesys Cloud, or you are creating a role that allows a specific user to invoke the Lambda. For the aws:execute action to work securely, the AWS credentials stored in Genesys Cloud must belong to an IAM User or Role that has the lambda:InvokeFunction permission.

Here, we create an IAM Role and attach a policy that allows invoking a specific Lambda function.

import json

def create_lambda_invoke_role(role_name, lambda_arn):
    """
    Creates an IAM role with a policy allowing invocation of a specific Lambda function.
    
    Args:
        role_name (str): Name of the IAM role to create.
        lambda_arn (str): ARN of the Lambda function to allow invocation for.
    """
    
    # 1. Define the Trust Policy
    # This allows the role to be assumed by services or users as needed.
    # For Genesys Cloud integration, the credentials stored in Genesys are typically 
    # IAM User keys or Role keys assumed by a service account.
    trust_policy = {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Principal": {
                    "AWS": "*"  # In production, restrict this to the specific IAM User/Role used in Genesys
                },
                "Action": "sts:AssumeRole"
            }
        ]
    }
    
    try:
        iam_client.create_role(
            RoleName=role_name,
            AssumeRolePolicyDocument=json.dumps(trust_policy),
            Description="Role for Genesys Cloud to invoke Lambda"
        )
        print(f"Role {role_name} created successfully.")
    except iam_client.exceptions.EntityAlreadyExistsException:
        print(f"Role {role_name} already exists. Skipping creation.")

    # 2. Define the Inline Policy for Lambda Invocation
    policy_document = {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Sid": "AllowLambdaInvoke",
                "Effect": "Allow",
                "Action": "lambda:InvokeFunction",
                "Resource": lambda_arn
            }
        ]
    }
    
    policy_name = "AllowGenesysLambdaInvoke"
    
    try:
        iam_client.put_role_policy(
            RoleName=role_name,
            PolicyName=policy_name,
            PolicyDocument=json.dumps(policy_document)
        )
        print(f"Policy {policy_name} attached to role {role_name}.")
    except Exception as e:
        print(f"Failed to attach policy: {e}")

# Example Usage
LAMBDA_ARN = "arn:aws:lambda:us-east-1:123456789012:function:MyGenesysFunction"
ROLE_NAME = "GenesysCloudLambdaInvoker"

create_lambda_invoke_role(ROLE_NAME, LAMBDA_ARN)

Step 2: Configure AWS Credentials in Genesys Cloud

Before using the data action, you must store the AWS credentials (Access Key and Secret Key) in Genesys Cloud. These credentials must belong to the IAM User or Role created in Step 1.

You do this via the Genesys Cloud Admin Console or the API. Here is the API approach to create/update an AWS credential set.

def configure_aws_credential_set(credential_set_id, access_key, secret_key, region):
    """
    Updates an existing AWS Credential Set in Genesys Cloud.
    
    Args:
        credential_set_id (str): The ID of the credential set in Genesys Cloud.
        access_key (str): AWS Access Key ID.
        secret_key (str): AWS Secret Access Key.
        region (str): AWS Region (e.g., 'us-east-1').
    """
    url = f"https://api.{ENVIRONMENT}/api/v2/integrations/credentialsets/{credential_set_id}"
    
    payload = {
        "name": "GenesysAWSLambdaCreds",
        "providerName": "aws",
        "credentials": {
            "accessKey": access_key,
            "secretKey": secret_key
        },
        "region": region
    }
    
    response = requests.put(url, headers=genesys_headers, json=payload)
    
    if response.status_code in [200, 201]:
        print("AWS Credential Set updated successfully.")
        return response.json()
    else:
        raise Exception(f"Failed to update credential set: {response.text}")

# Note: You must first create a credential set via the Admin Console or API 
# to get the credential_set_id. This example assumes you have it.
# CREDENTIAL_SET_ID = "your-credential-set-id-from-genesis"
# configure_aws_credential_set(CREDENTIAL_SET_ID, "AKIA...", "secret...", "us-east-1")

Step 3: Create the Architect Flow with the Data Action

Now, you create the Architect flow that contains the aws:execute data action. This action triggers the Lambda function.

The aws:execute action requires:

• credentialSetId: The ID of the AWS credential set configured in Genesys.
• service: The AWS service (e.g., lambda).
• action: The specific operation (e.g., invoke).
• parameters: The input to the Lambda function.

import uuid

def create_architect_flow_with_lambda(credential_set_id, lambda_function_name, lambda_payload):
    """
    Creates an Architect flow that invokes a Lambda function using a data action.
    
    Args:
        credential_set_id (str): The ID of the AWS credential set in Genesys.
        lambda_function_name (str): The name of the Lambda function.
        lambda_payload (dict): The payload to send to the Lambda function.
    """
    
    # Generate a unique flow ID
    flow_id = str(uuid.uuid4())
    
    # Define the flow structure
    flow_definition = {
        "id": flow_id,
        "name": "Lambda Invocation Flow",
        "description": "Flow that triggers an AWS Lambda function via data action",
        "type": "outbound",  # Or 'inbound', 'chat', etc.
        "draft": True,
        "published": False,
        "entities": {
            "triggers": [
                {
                    "id": "trigger-1",
                    "type": "webhook",
                    "configuration": {
                        "url": "/webhook/lambda-trigger"
                    }
                }
            ],
            "actions": [
                {
                    "id": "action-lambda",
                    "type": "data",
                    "configuration": {
                        "actions": [
                            {
                                "id": "invoke-lambda",
                                "type": "aws:execute",
                                "configuration": {
                                    "credentialSetId": credential_set_id,
                                    "service": "lambda",
                                    "action": "invoke",
                                    "parameters": {
                                        "functionName": lambda_function_name,
                                        "payload": lambda_payload,
                                        "invocationType": "RequestResponse"
                                    }
                                },
                                "outcomes": {
                                    "success": {
                                        "nextActionId": "action-end"
                                    },
                                    "error": {
                                        "nextActionId": "action-log-error"
                                    }
                                }
                            }
                        ]
                    }
                },
                {
                    "id": "action-log-error",
                    "type": "log",
                    "configuration": {
                        "message": "Lambda invocation failed: {{data.aws.execute.error.message}}"
                    }
                },
                {
                    "id": "action-end",
                    "type": "end",
                    "configuration": {
                        "status": "success"
                    }
                }
            ]
        }
    }
    
    url = f"https://api.{ENVIRONMENT}/api/v2/flows/{flow_id}"
    response = requests.post(url, headers=genesys_headers, json=flow_definition)
    
    if response.status_code in [200, 201, 204]:
        print(f"Flow {flow_id} created successfully.")
        return response.json()
    else:
        raise Exception(f"Failed to create flow: {response.text}")

# Example Usage
# LAMBDA_PAYLOAD = {"key": "value", "agentId": "{{contact.attributes.agentId}}"}
# create_architect_flow_with_lambda(CREDENTIAL_SET_ID, "MyGenesysFunction", LAMBDA_PAYLOAD)

Step 4: Processing Results and Handling Errors

The aws:execute action returns the Lambda response in the data.aws.execute context. You can access the Lambda’s return value via {{data.aws.execute.result}}.

If the Lambda invocation fails (e.g., timeout, handler error), the action routes to the error outcome. You should log the error for debugging.

# Example of how to parse the Lambda response in a subsequent action
# This is conceptual JSON for an Architect flow action that logs the result

log_action_payload = {
    "id": "action-log-result",
    "type": "log",
    "configuration": {
        "message": "Lambda Result: {{data.aws.execute.result}}"
    }
}

Complete Working Example

Here is a complete Python script that combines IAM role creation, Genesys token retrieval, and flow creation. You must replace the placeholder values with your actual credentials.

import os
import json
import uuid
import requests
import boto3
import base64

# Configuration
AWS_REGION = os.environ.get('AWS_DEFAULT_REGION', 'us-east-1')
GENESYS_CLIENT_ID = os.environ['GENESYS_CLIENT_ID']
GENESYS_CLIENT_SECRET = os.environ['GENESYS_CLIENT_SECRET']
GENESYS_ENVIRONMENT = os.environ['GENESYS_ENVIRONMENT']
CREDENTIAL_SET_ID = os.environ['GENESYS_AWS_CREDENTIAL_SET_ID']
LAMBDA_ARN = os.environ['AWS_LAMBDA_ARN']
LAMBDA_NAME = os.environ['AWS_LAMBDA_NAME']
ROLE_NAME = "GenesysCloudLambdaInvoker"

def get_genesys_token():
    auth_url = f"https://api.{GENESYS_ENVIRONMENT}/oauth/token"
    credentials = f"{GENESYS_CLIENT_ID}:{GENESYS_CLIENT_SECRET}"
    encoded_credentials = base64.b64encode(credentials.encode('utf-8')).decode('utf-8')
    headers = {'Authorization': f'Basic {encoded_credentials}', 'Content-Type': 'application/x-www-form-urlencoded'}
    data = {'grant_type': 'client_credentials', 'scope': 'flow:create flow:view flow:edit'}
    response = requests.post(auth_url, headers=headers, data=data)
    if response.status_code != 200:
        raise Exception(f"Failed to get Genesys token: {response.text}")
    return response.json()['access_token']

def create_iam_role():
    iam_client = boto3.client('iam', region_name=AWS_REGION)
    trust_policy = {
        "Version": "2012-10-17",
        "Statement": [{"Effect": "Allow", "Principal": {"AWS": "*"}, "Action": "sts:AssumeRole"}]
    }
    policy_document = {
        "Version": "2012-10-17",
        "Statement": [{"Sid": "AllowLambdaInvoke", "Effect": "Allow", "Action": "lambda:InvokeFunction", "Resource": LAMBDA_ARN}]
    }
    try:
        iam_client.create_role(RoleName=ROLE_NAME, AssumeRolePolicyDocument=json.dumps(trust_policy))
        print(f"Role {ROLE_NAME} created.")
    except iam_client.exceptions.EntityAlreadyExistsException:
        print(f"Role {ROLE_NAME} already exists.")
    
    iam_client.put_role_policy(RoleName=ROLE_NAME, PolicyName="AllowGenesysLambdaInvoke", PolicyDocument=json.dumps(policy_document))
    print(f"Policy attached to {ROLE_NAME}.")

def create_flow():
    token = get_genesys_token()
    headers = {'Authorization': f'Bearer {token}', 'Content-Type': 'application/json'}
    flow_id = str(uuid.uuid4())
    
    flow_definition = {
        "id": flow_id,
        "name": "Lambda Invocation Flow",
        "type": "outbound",
        "draft": True,
        "entities": {
            "triggers": [{"id": "trigger-1", "type": "webhook", "configuration": {"url": "/webhook/trigger"}}],
            "actions": [
                {
                    "id": "action-lambda",
                    "type": "data",
                    "configuration": {
                        "actions": [
                            {
                                "id": "invoke-lambda",
                                "type": "aws:execute",
                                "configuration": {
                                    "credentialSetId": CREDENTIAL_SET_ID,
                                    "service": "lambda",
                                    "action": "invoke",
                                    "parameters": {
                                        "functionName": LAMBDA_NAME,
                                        "payload": {"source": "genesys"},
                                        "invocationType": "RequestResponse"
                                    }
                                },
                                "outcomes": {
                                    "success": {"nextActionId": "action-end"},
                                    "error": {"nextActionId": "action-log-error"}
                                }
                            }
                        ]
                    }
                },
                {
                    "id": "action-log-error",
                    "type": "log",
                    "configuration": {"message": "Lambda Error: {{data.aws.execute.error.message}}"}
                },
                {
                    "id": "action-end",
                    "type": "end",
                    "configuration": {"status": "success"}
                }
            ]
        }
    }
    
    url = f"https://api.{GENESYS_ENVIRONMENT}/api/v2/flows/{flow_id}"
    response = requests.post(url, headers=headers, json=flow_definition)
    if response.status_code in [200, 201, 204]:
        print(f"Flow {flow_id} created successfully.")
    else:
        raise Exception(f"Failed to create flow: {response.text}")

if __name__ == "__main__":
    create_iam_role()
    create_flow()

Common Errors & Debugging

Error: 403 Forbidden on AWS Lambda Invocation

• What causes it: The IAM User or Role associated with the AWS credentials in Genesys Cloud does not have the lambda:InvokeFunction permission for the specified Lambda ARN.
• How to fix it: Ensure the IAM policy attached to the role/user includes the correct ARN. Check for typos in the ARN.
• Code showing the fix:

  # Ensure the resource ARN matches exactly
  policy_document = {
      "Version": "2012-10-17",
      "Statement": [
          {
              "Effect": "Allow",
              "Action": "lambda:InvokeFunction",
              "Resource": "arn:aws:lambda:us-east-1:123456789012:function:ExactFunctionName"
          }
      ]
  }
  

Error: 401 Unauthorized in Genesys Cloud

• What causes it: The OAuth token used to create the flow is invalid or expired.
• How to fix it: Refresh the token using the client credentials flow. Ensure the token has the flow:create scope.

Error: 429 Too Many Requests

• What causes it: You are exceeding the rate limit for Genesys Cloud API calls.
• How to fix it: Implement exponential backoff in your Python script.
• Code showing the fix:

  import time
  
  def make_request_with_retry(url, headers, json_data, max_retries=3):
      for attempt in range(max_retries):
          response = requests.post(url, headers=headers, json=json_data)
          if response.status_code == 429:
              wait_time = 2 ** attempt
              print(f"Rate limited. Waiting {wait_time} seconds.")
              time.sleep(wait_time)
          else:
              return response
      raise Exception("Max retries exceeded")
  

Official References

• Genesys Cloud Architect API Documentation
• AWS IAM Policy Examples
• AWS Lambda Invoke Function API
• Genesys Cloud OAuth 2.0 Client Credentials Flow