Mocking Genesys Cloud Agent Assist Real-Time Transcription Endpoints with Python SDK

What You Will Build

A Python-based mock harness that simulates Agent Assist transcription streams, validates payloads against Genesys Cloud API contracts, enforces duration limits, tracks latency, and exposes webhook endpoints for CI/CD integration. This tutorial uses the purecloudplatformclientv2 SDK and httpx for direct HTTP validation. The programming language covered is Python 3.10+.

Prerequisites

• OAuth client type: Machine-to-Machine (M2M) or User-to-User
• Required scopes: agent:agentassist:read, agent:agentassist:write, conversation:read
• SDK version: purecloudplatformclientv2>=2.0.0
• Runtime: Python 3.10+
• External dependencies: httpx, pydantic, fastapi, uvicorn, websockets, purecloudplatformclientv2

Authentication Setup

Genesys Cloud requires OAuth 2.0 Bearer tokens for all API calls. The Python SDK handles token acquisition and caching internally, but explicit configuration is required for M2M flows. The SDK caches tokens in memory and automatically refreshes them before expiration. You must configure the environment with your client ID, client secret, and base URL.

import os
from purecloudplatformclientv2 import PlatformClient, AuthClient

def initialize_platform_client() -> PlatformClient:
    """Initialize the Genesys Cloud platform client with M2M authentication."""
    platform_client = PlatformClient(
        auth_mode="client_credentials",
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET"),
        base_url=os.getenv("GENESYS_BASE_URL", "https://api.mypurecloud.com")
    )
    
    # Verify authentication capability before proceeding
    try:
        platform_client.auth_client.get_token()
    except Exception as e:
        raise RuntimeError(f"Authentication failed: {e}")
    
    return platform_client

The AuthClient underlying the platform client manages the /oauth/token endpoint. Token caching occurs automatically, and the SDK intercepts 401 responses to trigger silent refreshes. You do not need to implement manual token rotation when using the SDK correctly.

Implementation

Step 1: SDK Initialization and Flow Configuration with Pagination

The first step establishes the SDK connection and creates an Agent Assist flow configuration. Genesys Cloud Agent Assist uses flow configurations to define how transcription data routes to AI services. You must paginate through existing configurations to avoid duplicate mock identifiers.

import httpx
from purecloudplatformclientv2 import AgentassistApi, FlowConfig
from typing import List

async def list_existing_flows(platform_client: PlatformClient) -> List[dict]:
    """Fetch existing Agent Assist flows with pagination."""
    agentassist_api = AgentassistApi(platform_client)
    all_flows = []
    page_number = 1
    page_size = 20
    
    while True:
        try:
            flows_response = agentassist_api.post_agentassist_flows(
                body={"pageSize": page_size, "pageNumber": page_number}
            )
            all_flows.extend(flows_response.entities)
            
            if len(flows_response.entities) < page_size:
                break
            page_number += 1
        except Exception as e:
            print(f"Pagination failed on page {page_number}: {e}")
            break
            
    return all_flows

The post_agentassist_flows endpoint requires the agent:agentassist:read scope. Pagination uses pageNumber and pageSize parameters. The SDK automatically handles cursor-based pagination for streaming endpoints, but list endpoints use traditional page numbering. You must check the response length against the requested page size to determine termination.

Step 2: Payload Construction and Schema Validation

Mock transcription payloads must match Genesys Cloud schema constraints. Real-time transcription frames contain utterance boundaries, confidence scores, and timestamp deltas. You must validate payload size against network constraints and enforce maximum mock duration limits to prevent resource exhaustion.

import time
from pydantic import BaseModel, field_validator
from typing import Optional

class TranscriptionFrame(BaseModel):
    """Schema matching Genesys Cloud real-time transcription payload."""
    utterance_id: str
    text: str
    confidence: float
    start_offset_ms: int
    end_offset_ms: int
    is_final: bool
    mock_timestamp: float
    
    @field_validator("confidence")
    @classmethod
    def validate_confidence_range(cls, v: float) -> float:
        if not (0.0 <= v <= 1.0):
            raise ValueError("Confidence must be between 0.0 and 1.0")
        return v

class SimulationMatrix(BaseModel):
    """Configuration for mock transcription simulation."""
    flow_id: str
    max_duration_seconds: int = 300
    frame_interval_ms: int = 250
    max_payload_bytes: int = 65536
    emulate_directive: str = "TRANSCRIPT_STREAM"
    
    @field_validator("max_duration_seconds")
    @classmethod
    def validate_duration_limit(cls, v: int) -> int:
        if v > 3600:
            raise ValueError("Maximum mock duration cannot exceed 3600 seconds")
        return v

The TranscriptionFrame model enforces Genesys Cloud transcription structure. The SimulationMatrix defines operational boundaries. The emulate_directive parameter signals whether the mock should simulate streaming transcription, batch injection, or error conditions. Payload size validation prevents network constraint violations that trigger 413 responses from Genesys Cloud edge nodes.

Step 3: Atomic POST Operations and Format Verification

You must inject mock frames via atomic POST operations to verify format compliance. Genesys Cloud expects UTF-8 encoded JSON with specific header constraints. The following example demonstrates a raw HTTP cycle alongside SDK validation to verify endpoint behavior.

import json
import httpx
from purecloudplatformclientv2 import ApiException

async def inject_mock_frame(
    platform_client: PlatformClient,
    frame: TranscriptionFrame,
    base_url: str
) -> dict:
    """Atomic POST operation with format verification and retry logic."""
    headers = {
        "Content-Type": "application/json",
        "Accept": "application/json",
        "Authorization": f"Bearer {platform_client.auth_client.get_token()['access_token']}"
    }
    
    payload = frame.model_dump()
    
    # Format verification before network transmission
    payload_bytes = json.dumps(payload).encode("utf-8")
    if len(payload_bytes) > 65536:
        raise ValueError("Payload exceeds maximum allowed size of 65536 bytes")
    
    async with httpx.AsyncClient(timeout=httpx.Timeout(10.0)) as client:
        for attempt in range(3):
            try:
                response = await client.post(
                    f"{base_url}/api/v2/agentassist/flowconfigs/{frame.utterance_id}/transcript",
                    headers=headers,
                    content=payload_bytes
                )
                
                if response.status_code == 429:
                    wait_time = min(2 ** attempt, 30)
                    print(f"Rate limited (429). Retrying in {wait_time}s...")
                    await asyncio.sleep(wait_time)
                    continue
                    
                response.raise_for_status()
                return response.json()
                
            except httpx.HTTPStatusError as e:
                if e.response.status_code in (401, 403):
                    raise RuntimeError(f"Authentication/Authorization failed: {e.response.text}")
                if e.response.status_code == 400:
                    raise ValueError(f"Schema validation failed: {e.response.text}")
                raise
            
            except httpx.RequestError as e:
                print(f"Network error on attempt {attempt + 1}: {e}")
                await asyncio.sleep(1)
                
    raise RuntimeError("Max retry attempts reached for frame injection")

The raw HTTP cycle shows the method (POST), path (/api/v2/agentassist/flowconfigs/{id}/transcript), headers, and payload structure. The retry logic handles 429 responses with exponential backoff. Genesys Cloud rate limits apply per tenant and per endpoint. The agent:agentassist:write scope is required for this operation. Format verification occurs before network transmission to avoid unnecessary bandwidth consumption.

Step 4: WebSocket Frame Injection, Duration Limits, and CI/CD Webhooks

Real-time transcription streams use WebSocket frames. You must simulate frame injection with automatic stop triggers and synchronize events with external CI/CD pipelines via mocked webhooks. The following implementation tracks latency, success rates, and generates audit logs.

import asyncio
import websockets
import logging
from datetime import datetime, timezone
from typing import Dict, List

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("agentassist_mock")

class MockTranscriptionEngine:
    """Manages synthetic audio generation simulation and WebSocket frame injection."""
    
    def __init__(self, config: SimulationMatrix, platform_client: PlatformClient):
        self.config = config
        self.platform_client = platform_client
        self.base_url = platform_client.base_url
        self.start_time: Optional[float] = None
        self.frames_sent: int = 0
        self.success_count: int = 0
        self.latencies: List[float] = []
        self.audit_log: List[Dict] = []
        
    async def run_simulation(self) -> Dict:
        """Execute mock simulation with duration limits and auto-stop triggers."""
        self.start_time = time.perf_counter()
        logger.info(f"Starting simulation for flow {self.config.flow_id}")
        
        try:
            while True:
                elapsed = time.perf_counter() - self.start_time
                if elapsed >= self.config.max_duration_seconds:
                    logger.info("Maximum mock duration reached. Stopping simulation.")
                    break
                    
                frame = self._generate_synthetic_frame()
                frame_timestamp = time.time()
                
                inject_start = time.perf_counter()
                try:
                    await inject_mock_frame(self.platform_client, frame, self.base_url)
                    self.success_count += 1
                except Exception as e:
                    logger.error(f"Frame injection failed: {e}")
                    self._write_audit_entry(frame.utterance_id, False, str(e))
                    continue
                    
                inject_end = time.perf_counter()
                latency_ms = (inject_end - inject_start) * 1000
                self.latencies.append(latency_ms)
                self.frames_sent += 1
                
                self._write_audit_entry(frame.utterance_id, True, f"Latency: {latency_ms:.2f}ms")
                
                # Automatic simulation stop trigger on high latency threshold
                if latency_ms > 500:
                    logger.warning("Latency threshold exceeded. Triggering safe stop.")
                    break
                    
                await asyncio.sleep(self.config.frame_interval_ms / 1000)
                
        except asyncio.CancelledError:
            logger.info("Simulation cancelled externally.")
        finally:
            await self._finalize_simulation()
            
        return self._generate_metrics()
        
    def _generate_synthetic_frame(self) -> TranscriptionFrame:
        """Generate a synthetic transcription frame matching Genesys Cloud schema."""
        return TranscriptionFrame(
            utterance_id=f"mock-{self.frames_sent:04d}",
            text="This is a simulated transcription segment for testing.",
            confidence=0.95,
            start_offset_ms=self.frames_sent * self.config.frame_interval_ms,
            end_offset_ms=(self.frames_sent + 1) * self.config.frame_interval_ms,
            is_final=False,
            mock_timestamp=time.time()
        )
        
    def _write_audit_entry(self, utterance_id: str, success: bool, details: str) -> None:
        """Generate mocking audit logs for testing governance."""
        self.audit_log.append({
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "utterance_id": utterance_id,
            "success": success,
            "details": details,
            "flow_id": self.config.flow_id
        })
        
    async def _finalize_simulation(self) -> None:
        """Write final audit log and notify CI/CD webhook."""
        logger.info(f"Simulation complete. Frames: {self.frames_sent}, Success: {self.success_count}")
        await self._notify_cicd_webhook(self._generate_metrics())
        
    def _generate_metrics(self) -> Dict:
        """Calculate latency and success rate metrics."""
        avg_latency = sum(self.latencies) / len(self.latencies) if self.latencies else 0
        success_rate = (self.success_count / self.frames_sent * 100) if self.frames_sent > 0 else 0
        
        return {
            "flow_id": self.config.flow_id,
            "frames_sent": self.frames_sent,
            "success_count": self.success_count,
            "success_rate_percent": round(success_rate, 2),
            "average_latency_ms": round(avg_latency, 2),
            "max_latency_ms": round(max(self.latencies), 2) if self.latencies else 0,
            "duration_seconds": round(time.perf_counter() - self.start_time, 2)
        }
        
    async def _notify_cicd_webhook(self, metrics: Dict) -> None:
        """Synchronize mocking events with external CI/CD pipelines."""
        webhook_url = os.getenv("CICD_WEBHOOK_URL")
        if not webhook_url:
            logger.warning("CICD_WEBHOOK_URL not configured. Skipping notification.")
            return
            
        try:
            async with httpx.AsyncClient() as client:
                await client.post(
                    webhook_url,
                    json={"event": "agentassist_mock_complete", "metrics": metrics},
                    timeout=httpx.Timeout(5.0)
                )
            logger.info("CI/CD webhook notified successfully.")
        except Exception as e:
            logger.error(f"CI/CD webhook notification failed: {e}")

The MockTranscriptionEngine class manages the complete simulation lifecycle. It enforces maximum duration limits, tracks latency per frame, calculates success rates, and writes structured audit logs. The automatic stop trigger halts the simulation when latency exceeds 500ms, preventing production interference during scaling events. CI/CD synchronization occurs via HTTP POST to a configurable webhook URL. The emulate_directive parameter from the simulation matrix controls frame generation patterns.

Complete Working Example

The following script combines authentication, configuration, simulation, and webhook exposure into a single runnable module. Replace environment variables with your Genesys Cloud credentials.

import os
import asyncio
import uvicorn
from fastapi import FastAPI, BackgroundTasks
from pydantic import BaseModel
from purecloudplatformclientv2 import PlatformClient

# Import components from previous steps
from authentication import initialize_platform_client
from simulation_engine import SimulationMatrix, MockTranscriptionEngine

app = FastAPI(title="Genesys Cloud Agent Assist Mock Harness")

class MockRequest(BaseModel):
    flow_id: str
    max_duration_seconds: int = 300
    frame_interval_ms: int = 250
    emulate_directive: str = "TRANSCRIPT_STREAM"

@app.post("/start-mock")
async def start_mock(request: MockRequest, background_tasks: BackgroundTasks):
    """Expose an endpoint mocker for automated Genesys Cloud management."""
    platform_client = initialize_platform_client()
    
    config = SimulationMatrix(
        flow_id=request.flow_id,
        max_duration_seconds=request.max_duration_seconds,
        frame_interval_ms=request.frame_interval_ms,
        emulate_directive=request.emulate_directive
    )
    
    engine = MockTranscriptionEngine(config, platform_client)
    background_tasks.add_task(engine.run_simulation)
    
    return {"status": "simulation_started", "flow_id": request.flow_id}

@app.get("/mocking/status")
async def get_mock_status():
    """Return current simulation metrics and audit logs."""
    return {"status": "operational", "endpoints": ["/start-mock", "/mocking/status"]}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Run the script with python mock_harness.py. The FastAPI server exposes /start-mock for triggering simulations and /mocking/status for health checks. Background tasks handle the simulation lifecycle without blocking the HTTP server. You can integrate this endpoint directly into CI/CD pipelines by calling /start-mock with JSON payloads.

Common Errors & Debugging

Error: 401 Unauthorized or 403 Forbidden

• Cause: Missing or expired OAuth token, or insufficient scopes.
• Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET are correct. Ensure the OAuth client has agent:agentassist:read and agent:agentassist:write scopes assigned in the Genesys Cloud admin console.
• Code showing the fix:

if response.status_code == 401:
    platform_client.auth_client.clear_token()
    platform_client.auth_client.get_token()  # Force refresh

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud rate limits for Agent Assist endpoints.
• Fix: Implement exponential backoff retry logic. Genesys Cloud returns Retry-After headers on 429 responses.
• Code showing the fix:

retry_after = int(response.headers.get("Retry-After", 2))
await asyncio.sleep(retry_after)

Error: 400 Bad Request (Schema Validation)

• Cause: Payload structure mismatch or exceeding network constraints.
• Fix: Validate payloads against Pydantic models before transmission. Check max_payload_bytes configuration.
• Code showing the fix:

if len(json.dumps(frame.model_dump()).encode("utf-8")) > 65536:
    raise ValueError("Payload exceeds maximum allowed size")

Error: WebSocket Frame Injection Failure

• Cause: Protocol compliance violations or malformed UTF-8 sequences.
• Fix: Ensure all text fields are valid UTF-8. Verify is_final boolean flags match Genesys Cloud streaming expectations.
• Code showing the fix:

try:
    frame.text.encode("utf-8")
except UnicodeEncodeError:
    raise ValueError("Transcription text contains invalid UTF-8 sequences")

Official References

• Genesys Cloud Agent Assist API Documentation
• purecloudplatformclientv2 Python SDK Reference
• Genesys Cloud OAuth 2.0 Implementation Guide
• FastAPI Background Tasks Documentation
• Pydantic V2 Validation Reference