Training Genesys Cloud Agent Assist Custom Models via Python SDK

What You Will Build

• A production-grade Python module that constructs, validates, and submits training jobs for Genesys Cloud Agent Assist custom models.
• The module uses the genesyscloud Python SDK and the /api/v2/analytics/agentassist/models/{modelId}/train endpoint.
• The implementation covers Python 3.9+ with Pydantic schema validation, async callback handling, metric tracking, and governance audit logging.

Prerequisites

• OAuth2 client credentials with scopes: agentassist:custommodel:write, agentassist:custommodel:read, agentassist:dataset:read
• SDK version: genesyscloud>=2.40.0
• Runtime: Python 3.9+
• Dependencies: genesyscloud, httpx, pydantic>=2.0, aiohttp, structlog, uuid
• A deployed Agent Assist custom model in READY or TRAINING_COMPLETE state
• A valid dataset ID in the same Genesys Cloud organization

Authentication Setup

Genesys Cloud uses OAuth2 client credentials flow. The token must be cached and refreshed before expiration. The SDK accepts a token provider function.

import httpx
import time
from typing import Optional

class GenesysOAuthProvider:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{environment}/oauth/token"
        self._token: Optional[str] = None
        self._expires_at: float = 0

    def get_token(self) -> str:
        if self._token and time.time() < self._expires_at - 30:
            return self._token

        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        data = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "agentassist:custommodel:write agentassist:custommodel:read agentassist:dataset:read"
        }

        with httpx.Client() as client:
            response = client.post(self.token_url, headers=headers, data=data)
            response.raise_for_status()
            payload = response.json()
            self._token = payload["access_token"]
            self._expires_at = time.time() + payload["expires_in"]
            return self._token

Implementation

Step 1: Validation Pipeline & Schema Verification

Training failures typically occur from malformed payloads, oversized datasets, or missing resource quotas. This step validates the training request against Genesys Cloud ML pipeline constraints before submission.

import uuid
from pydantic import BaseModel, field_validator, ValidationError
from typing import Dict, Any, Optional

MAX_DATASET_SIZE_MB = 500
MAX_RECORDS = 200000

class TrainingParameters(BaseModel):
    epochs: int = 10
    learning_rate: float = 0.001
    validation_split: float = 0.2
    batch_size: int = 32

    @field_validator("epochs")
    @classmethod
    def validate_epochs(cls, v: int) -> int:
        if not 1 <= v <= 100:
            raise ValueError("Epochs must be between 1 and 100")
        return v

    @field_validator("validation_split")
    @classmethod
    def validate_split(cls, v: float) -> float:
        if not 0.1 <= v <= 0.5:
            raise ValueError("Validation split must be between 0.1 and 0.5")
        return v

class TrainingJobPayload(BaseModel):
    model_id: str
    dataset_id: str
    parameters: TrainingParameters
    callback_uri: Optional[str] = None

    @field_validator("model_id", "dataset_id")
    @classmethod
    def validate_uuid_format(cls, v: str) -> str:
        try:
            uuid.UUID(v)
        except ValueError:
            raise ValueError("Identifier must be a valid UUID")
        return v

    def validate_dataset_constraints(self, dataset_metadata: Dict[str, Any]) -> None:
        size_mb = dataset_metadata.get("sizeBytes", 0) / (1024 * 1024)
        record_count = dataset_metadata.get("recordCount", 0)

        if size_mb > MAX_DATASET_SIZE_MB:
            raise ValueError(f"Dataset exceeds maximum size limit of {MAX_DATASET_SIZE_MB}MB")
        if record_count > MAX_RECORDS:
            raise ValueError(f"Dataset exceeds maximum record limit of {MAX_RECORDS}")

Step 2: Payload Construction & Atomic Training POST

The training submission is an atomic POST operation. The SDK handles serialization, but you must implement retry logic for 429 rate limits and verify the response format. The API returns a TrainingJob object with a jobId and initial state.

HTTP Request Cycle Reference:

POST /api/v2/analytics/agentassist/models/{modelId}/train HTTP/1.1
Host: mypurecloud.com
Authorization: Bearer <access_token>
Content-Type: application/json

{
  "datasetId": "8f3a1c2d-4e5f-6a7b-8c9d-0e1f2a3b4c5d",
  "parameters": {
    "epochs": 15,
    "learningRate": 0.0005,
    "validationSplit": 0.2,
    "batchSize": 64
  },
  "callbackUri": "https://your-registry.example.com/webhooks/agentassist/training-complete"
}

HTTP Response:

{
  "jobId": "7a9b8c6d-5e4f-3a2b-1c0d-9e8f7a6b5c4d",
  "modelId": "8f3a1c2d-4e5f-6a7b-8c9d-0e1f2a3b4c5d",
  "datasetId": "8f3a1c2d-4e5f-6a7b-8c9d-0e1f2a3b4c5d",
  "state": "QUEUED",
  "createdAt": "2024-01-15T10:30:00.000Z",
  "updatedAt": "2024-01-15T10:30:00.000Z",
  "metrics": {}
}

SDK Implementation with Retry Logic:

import time
import structlog
from genesyscloud import configuration, agentassist_api
from genesyscloud.models.training_job_request import TrainingJobRequest
from genesyscloud.rest import ApiException

logger = structlog.get_logger()

class GenesysAgentAssistTrainer:
    def __init__(self, oauth_provider: GenesysOAuthProvider):
        self.oauth = oauth_provider
        self.config = configuration.Configuration()
        self.config.access_token = oauth_provider.get_token()
        self.api_client = agentassist_api.AgentassistApi()

    def submit_training_job(self, payload: TrainingJobPayload) -> Dict[str, Any]:
        job_request = TrainingJobRequest(
            dataset_id=payload.dataset_id,
            parameters=payload.parameters.model_dump(),
            callback_uri=payload.callback_uri
        )

        retries = 0
        max_retries = 3
        base_delay = 2

        while retries <= max_retries:
            try:
                response = self.api_client.post_agentassist_model_train(
                    model_id=payload.model_id,
                    body=job_request
                )
                logger.info("Training job submitted successfully", job_id=response.job_id, state=response.state)
                return {
                    "jobId": response.job_id,
                    "state": response.state,
                    "submittedAt": response.created_at.isoformat() if response.created_at else None
                }
            except ApiException as e:
                if e.status == 429:
                    wait_time = base_delay * (2 ** retries)
                    logger.warning("Rate limited. Retrying...", delay=wait_time)
                    time.sleep(wait_time)
                    retries += 1
                elif e.status == 401:
                    self.config.access_token = self.oauth.get_token()
                    retries += 1
                else:
                    logger.error("Training submission failed", status=e.status, message=e.body)
                    raise
            except Exception as e:
                logger.error("Unexpected error during submission", error=str(e))
                raise

Step 3: Callback Handler, Metric Logging & Audit Generation

Genesys Cloud invokes the callbackUri when training completes. The handler must parse the webhook payload, calculate latency, extract accuracy metrics, update the external registry, and write an immutable audit log.

import json
import asyncio
import aiohttp
from datetime import datetime, timezone
from pathlib import Path
from typing import List

class TrainingCallbackHandler:
    def __init__(self, audit_log_path: str = "training_audit.jsonl"):
        self.audit_log_path = Path(audit_log_path)
        self.audit_log_path.touch()

    async def handle_webhook(self, request: aiohttp.web.Request) -> aiohttp.web.Response:
        try:
            body = await request.json()
            job_id = body.get("jobId")
            state = body.get("state")
            metrics = body.get("metrics", {})
            completed_at = body.get("completedAt")
            created_at = body.get("createdAt")

            if state not in ("COMPLETED", "FAILED"):
                return aiohttp.web.json_response({"status": "ignored"}, status=200)

            latency_seconds = None
            if completed_at and created_at:
                t_end = datetime.fromisoformat(completed_at.replace("Z", "+00:00"))
                t_start = datetime.fromisoformat(created_at.replace("Z", "+00:00"))
                latency_seconds = (t_end - t_start).total_seconds()

            accuracy_score = metrics.get("accuracy", 0.0)
            success_rate = metrics.get("successRate", 0.0)

            audit_record = {
                "timestamp": datetime.now(timezone.utc).isoformat(),
                "jobId": job_id,
                "state": state,
                "latencySeconds": latency_seconds,
                "accuracyScore": accuracy_score,
                "successRate": success_rate,
                "registrySync": True
            }

            self._write_audit_log(audit_record)
            await self._sync_external_registry(job_id, audit_record)

            return aiohttp.web.json_response({"status": "processed"}, status=200)
        except Exception as e:
            logger.error("Callback processing failed", error=str(e))
            return aiohttp.web.json_response({"status": "error", "message": str(e)}, status=500)

    def _write_audit_log(self, record: Dict[str, Any]) -> None:
        with open(self.audit_log_path, "a", encoding="utf-8") as f:
            f.write(json.dumps(record, default=str) + "\n")

    async def _sync_external_registry(self, job_id: str, record: Dict[str, Any]) -> None:
        # Replace with actual registry API call
        logger.info("Registry synchronized", job_id=job_id, record=record)

Complete Working Example

The following module combines authentication, validation, submission, and callback handling into a single automated trainer. Run it with your OAuth credentials and model identifiers.

import asyncio
import aiohttp.web
import structlog
from typing import Optional

structlog.configure(
    processors=[
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ],
    wrapper_class=structlog.make_filtering_bound_logger("INFO"),
    context_class=dict,
    logger_factory=structlog.PrintLoggerFactory(),
    cache_logger_on_first_use=True,
)
logger = structlog.get_logger()

class AutomatedAgentAssistTrainer:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mypurecloud.com"):
        self.oauth = GenesysOAuthProvider(client_id, client_secret, environment)
        self.trainer = GenesysAgentAssistTrainer(self.oauth)
        self.callback_handler = TrainingCallbackHandler()

    async def run_training_pipeline(self, model_id: str, dataset_id: str, callback_uri: str) -> Optional[Dict]:
        try:
            # Step 1: Validate payload
            payload = TrainingJobPayload(
                model_id=model_id,
                dataset_id=dataset_id,
                parameters=TrainingParameters(epochs=20, learning_rate=0.0005, validation_split=0.2, batch_size=64),
                callback_uri=callback_uri
            )

            # Simulate dataset metadata fetch for quota verification
            # In production, replace with actual GET /api/v2/analytics/agentassist/datasets/{dataset_id}
            dataset_metadata = {"sizeBytes": 250 * 1024 * 1024, "recordCount": 150000}
            payload.validate_dataset_constraints(dataset_metadata)

            # Step 2: Submit atomic POST
            result = self.trainer.submit_training_job(payload)
            logger.info("Pipeline initiated", job_id=result["jobId"])
            return result

        except ValidationError as ve:
            logger.error("Schema validation failed", errors=ve.errors())
            return None
        except ValueError as ve:
            logger.error("Constraint validation failed", error=str(ve))
            return None
        except Exception as e:
            logger.error("Pipeline execution failed", error=str(e))
            return None

    def start_callback_server(self, port: int = 8899) -> None:
        app = aiohttp.web.Application()
        app.router.add_post("/webhook", self.callback_handler.handle_webhook)
        logger.info("Callback server starting", port=port)
        aiohttp.web.run_app(app, host="0.0.0.0", port=port)

if __name__ == "__main__":
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    MODEL_UUID = "8f3a1c2d-4e5f-6a7b-8c9d-0e1f2a3b4c5d"
    DATASET_UUID = "1a2b3c4d-5e6f-7a8b-9c0d-1e2f3a4b5c6d"
    CALLBACK_URL = "https://your-registry.example.com/webhooks/agentassist/training-complete"

    trainer = AutomatedAgentAssistTrainer(CLIENT_ID, CLIENT_SECRET)
    
    # Run training submission
    result = asyncio.run(trainer.run_training_pipeline(MODEL_UUID, DATASET_UUID, CALLBACK_URL))
    if result:
        logger.info("Training job queued", job_id=result["jobId"])
        # Start callback server in a separate thread or process for webhook reception
        # trainer.start_callback_server(port=8899)

Common Errors & Debugging

Error: 400 Bad Request

• Cause: Invalid UUID format, missing required fields in TrainingJobRequest, or dataset size exceeds Genesys Cloud limits.
• Fix: Validate all identifiers against UUID regex. Ensure datasetId references a dataset in READY state. Verify parameters keys match the ML pipeline schema.
• Code Fix: The TrainingJobPayload Pydantic model enforces UUID format and parameter bounds. Check ValidationError output for exact field mismatches.

Error: 403 Forbidden

• Cause: OAuth token missing agentassist:custommodel:write scope or the client lacks organizational permissions.
• Fix: Regenerate the token with the exact scope string. Verify the integration user has the “Agent Assist Administrator” role.
• Code Fix: The GenesysOAuthProvider explicitly requests the required scopes. If the error persists, inspect the token payload via https://mypurecloud.com/oauth/introspect.

Error: 429 Too Many Requests

• Cause: Exceeding the organizational rate limit for training submissions (typically 10 requests per minute per model).
• Fix: Implement exponential backoff. The submit_training_job method includes a retry loop with base_delay * (2 ** retries).
• Code Fix: Increase max_retries if your workload requires higher throughput, or stagger submissions using a queue.

Error: 413 Payload Too Large / Dataset Constraint Violation

• Cause: Dataset exceeds the 500MB or 200,000 record limit enforced by the ML pipeline.
• Fix: Preprocess the dataset to reduce size. Remove duplicate entries, compress audio/text features, or split into multiple training jobs.
• Code Fix: The validate_dataset_constraints method raises a ValueError before API submission. Adjust MAX_DATASET_SIZE_MB only if your organization has an elevated quota.

Error: 500 Internal Server Error

• Cause: ML pipeline resource exhaustion or temporary platform outage.
• Fix: Check Genesys Cloud status dashboard. Retry after 60 seconds. Monitor the state field in the callback for FAILED transitions.
• Code Fix: The callback handler logs failures to training_audit.jsonl with latency and accuracy metrics for post-mortem analysis.

Official References

• Agent Assist Custom Models API Documentation
• Training Job Request Schema
• Python SDK Reference
• OAuth 2.0 Client Credentials Flow