Inject Real-Time Co-Browsing Snapshots into Genesys Cloud Agent Assist Sessions Using Python

What You Will Build

This tutorial builds a Python service that constructs and injects co-browsing snapshot payloads into active Genesys Cloud Agent Assist sessions. It uses the Genesys Cloud Agent Assist API v2 (/api/v2/agentassist) and the official Python SDK. The code covers payload construction, schema validation, frequency throttling, PII redaction verification, atomic delivery, webhook synchronization, latency tracking, and audit logging.

Prerequisites

• Genesys Cloud OAuth confidential client with scopes: agentassist:guide:write, agentassist:session:write
• Genesys Cloud Python SDK version 12.0.0 or later (pip install genesys-cloud-sdk-python)
• Python 3.9 runtime
• External dependencies: httpx, pydantic, cryptography, datetime
• Active Agent Assist session ID from a live conversation or test environment

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow for server-to-server API access. The SDK handles token acquisition and caching automatically, but you must configure the client ID and secret explicitly. The following code initializes the platform client and validates connectivity before proceeding to injection logic.

import os
import httpx
from genesyscloud.platformclient.v2 import Configuration, PlatformClient, AgentassistApi

def init_genesys_client() -> PlatformClient:
    """Initialize Genesys Cloud SDK with OAuth client credentials."""
    config = Configuration(
        client_id=os.environ["GENESYS_CLIENT_ID"],
        client_secret=os.environ["GENESYS_CLIENT_SECRET"],
        base_url=os.environ.get("GENESYS_BASE_URL", "https://api.mypurecloud.com")
    )
    platform_client = PlatformClient(config)
    
    # Verify authentication by fetching a lightweight resource
    try:
        httpx.get(f"{config.base_url}/api/v2/health", timeout=5.0)
    except httpx.RequestError as err:
        raise ConnectionError(f"Genesys Cloud connectivity failed: {err}") from err
        
    return platform_client

OAuth scope requirement: agentassist:guide:write is mandatory for injecting snapshots. The SDK caches the access token in memory and automatically refreshes it before expiration. You do not need to implement manual token rotation unless you operate across multiple process boundaries.

Implementation

Step 1: Construct Inject Payloads with Snapshot UUID, Coordinate Matrix, and Redaction Directives

The Agent Assist API accepts guide payloads through POST /api/v2/agentassist/sessions/{sessionId}/guides. Each payload must contain a snapshot identifier, a coordinate matrix for browser alignment, and explicit redaction rules. The following function builds a compliant payload and validates it against browser security constraints and maximum frequency limits.

import time
import uuid
from typing import Dict, List, Any
from pydantic import BaseModel, Field, validator

class SnapshotInjectPayload(BaseModel):
    snapshot_uuid: str = Field(default_factory=lambda: str(uuid.uuid4()))
    coordinate_matrix: List[List[int]] = Field(..., description="2D coordinate mapping for canvas alignment")
    redaction_directives: List[Dict[str, int]] = Field(default_factory=list)
    image_url: str
    image_format: str = Field(default="png", regex=r"^(png|jpeg|webp)$")
    timestamp: float = Field(default_factory=time.time)
    frequency_token: float = Field(default=0.0)

    @validator("coordinate_matrix")
    def validate_matrix_dimensions(cls, v: List[List[int]]) -> List[List[int]]:
        if len(v) != 4 or any(len(row) != 2 for row in v):
            raise ValueError("Coordinate matrix must be a 4x2 array representing viewport corners")
        return v

    @validator("image_format")
    def validate_format_security(cls, v: str) -> str:
        allowed = {"png", "jpeg", "webp"}
        if v not in allowed:
            raise ValueError(f"Image format {v} violates browser security constraints. Allowed: {allowed}")
        return v

def build_inject_payload(session_id: str, image_url: str, matrix: List[List[int]], redactions: List[Dict[str, int]]) -> Dict[str, Any]:
    """Construct and validate the Agent Assist guide payload."""
    payload_model = SnapshotInjectPayload(
        coordinate_matrix=matrix,
        redaction_directives=redactions,
        image_url=image_url
    )
    
    return {
        "type": "image",
        "content": {
            "url": payload_model.image_url,
            "format": payload_model.image_format
        },
        "position": {
            "x": payload_model.coordinate_matrix[0][0],
            "y": payload_model.coordinate_matrix[0][1],
            "width": abs(payload_model.coordinate_matrix[2][0] - payload_model.coordinate_matrix[0][0]),
            "height": abs(payload_model.coordinate_matrix[2][1] - payload_model.coordinate_matrix[0][1])
        },
        "redactions": payload_model.redaction_directives,
        "metadata": {
            "snapshotUUID": payload_model.snapshot_uuid,
            "coordinateMatrix": payload_model.coordinate_matrix,
            "injectTimestamp": payload_model.timestamp
        }
    }

Expected response from Genesys Cloud:

{
  "id": "guide-a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "type": "image",
  "status": "delivered",
  "sessionId": "session-xyz-123",
  "createdTimestamp": "2024-01-15T10:30:00.000Z"
}

Error handling: The pydantic validator raises ValueError on malformed matrices or disallowed formats. Catch these exceptions before issuing the HTTP request to prevent unnecessary 400 responses.

Step 2: Implement PII Masking Verification and Resolution Scaling Pipelines

Before injection, the service must verify that redaction directives fully cover sensitive regions and that the image resolution matches the target agent viewport. The following pipeline performs PII boundary checking and resolution scaling verification.

import re
from typing import Tuple

PII_PATTERNS = [
    r"\b\d{3}-\d{2}-\d{4}\b",  # SSN
    r"\b\d{16}\b",              # Credit card
    r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"  # Email
]

def verify_pii_masking(image_url: str, redactions: List[Dict[str, int]]) -> bool:
    """Validate that redaction directives cover detected PII regions."""
    # In production, replace with OCR or image analysis service call
    # This example simulates boundary validation against known sensitive zones
    sensitive_zones = [
        {"x": 120, "y": 45, "width": 180, "height": 25},
        {"x": 120, "y": 90, "width": 150, "height": 20}
    ]
    
    for zone in sensitive_zones:
        covered = any(
            r["x"] <= zone["x"] and r["y"] <= zone["y"] and
            r["x"] + r["width"] >= zone["x"] + zone["width"] and
            r["y"] + r["height"] >= zone["y"] + zone["height"]
            for r in redactions
        )
        if not covered:
            return False
    return True

def verify_resolution_scaling(image_url: str, target_width: int, target_height: int) -> Tuple[int, int]:
    """Fetch image dimensions and verify scaling compatibility."""
    try:
        with httpx.stream("GET", image_url, timeout=10.0) as response:
            response.raise_for_status()
            # Extract Content-Length or use headers if available
            # For simplicity, we assume valid dimensions in this tutorial
            return target_width, target_height
    except httpx.HTTPError as err:
        raise RuntimeError(f"Resolution verification failed: {err}") from err

Error handling: If verify_pii_masking returns False, the injection pipeline must abort. If verify_resolution_scaling fails, log the mismatch and retry with a scaled variant. These checks prevent data exposure and canvas rendering failures.

Step 3: Atomic POST Delivery with Frequency Throttling and Webhook Synchronization

The injection operation must be atomic, respect maximum snapshot frequency limits, and synchronize with external screen sharing services. The following function implements retry logic for 429 responses, tracks latency, and triggers webhook callbacks.

import logging
from datetime import datetime, timezone

logger = logging.getLogger("agentassist.injector")

class InjectMetrics:
    def __init__(self):
        self.last_inject_time: float = 0.0
        self.success_count: int = 0
        self.failure_count: int = 0
        self.total_latency_ms: float = 0.0

METRICS = InjectMetrics()
MAX_FREQUENCY_HZ = 2.0  # Maximum 2 snapshots per second

def inject_snapshot_atomic(
    platform_client: PlatformClient,
    session_id: str,
    payload: Dict[str, Any],
    webhook_url: str
) -> Dict[str, Any]:
    """Atomically inject snapshot with frequency control, retry, and webhook sync."""
    current_time = time.time()
    elapsed = current_time - METRICS.last_inject_time
    
    if elapsed < (1.0 / MAX_FREQUENCY_HZ):
        raise RuntimeError("Maximum snapshot frequency limit exceeded. Throttling injection.")
    
    start_time = time.time()
    api = AgentassistApi(platform_client)
    
    try:
        # SDK call maps to POST /api/v2/agentassist/sessions/{sessionId}/guides
        response = api.post_agentassist_session_guide(session_id, body=payload)
        inject_latency_ms = (time.time() - start_time) * 1000
        
        METRICS.last_inject_time = current_time
        METRICS.success_count += 1
        METRICS.total_latency_ms += inject_latency_ms
        
        # Synchronize with external screen sharing service via webhook
        webhook_payload = {
            "event": "snapshot_injected",
            "sessionId": session_id,
            "snapshotUUID": payload["metadata"]["snapshotUUID"],
            "latencyMs": round(inject_latency_ms, 2),
            "timestamp": datetime.now(timezone.utc).isoformat()
        }
        
        httpx.post(webhook_url, json=webhook_payload, timeout=5.0)
        
        logger.info("Snapshot injected successfully. Latency: %.2f ms", inject_latency_ms)
        return {"status": "success", "guide_id": response.id, "latency_ms": inject_latency_ms}
        
    except Exception as err:
        inject_latency_ms = (time.time() - start_time) * 1000
        METRICS.failure_count += 1
        METRICS.total_latency_ms += inject_latency_ms
        
        # Handle 429 rate limit with exponential backoff
        if "429" in str(err) or "rate limit" in str(err).lower():
            backoff = min(2 ** 3, 10.0)  # Cap at 10 seconds
            logger.warning("Rate limit encountered. Retrying in %.1f seconds.", backoff)
            time.sleep(backoff)
            return inject_snapshot_atomic(platform_client, session_id, payload, webhook_url)
            
        raise RuntimeError(f"Injection failed after latency tracking: {err}") from err

Expected HTTP request cycle:

POST /api/v2/agentassist/sessions/session-xyz-123/guides HTTP/1.1
Host: api.mypurecloud.com
Authorization: Bearer <access_token>
Content-Type: application/json
Accept: application/json

{
  "type": "image",
  "content": {
    "url": "https://storage.example.com/snapshots/snap-123.png",
    "format": "png"
  },
  "position": {
    "x": 100,
    "y": 200,
    "width": 300,
    "height": 200
  },
  "redactions": [
    {"x": 120, "y": 45, "width": 180, "height": 25}
  ],
  "metadata": {
    "snapshotUUID": "snap-123",
    "coordinateMatrix": [[100,200],[400,200],[400,400],[100,400]],
    "injectTimestamp": 1705312200.0
  }
}

The API returns 201 Created with the guide identifier on success. The function automatically retries on 429 responses and forwards synchronization events to the configured webhook URL.

Step 4: Audit Logging and Render Success Rate Tracking

Governance requires persistent audit trails and efficiency metrics. The following utility generates structured audit logs and calculates render success rates over a rolling window.

from collections import deque
from typing import Optional

class AuditLogger:
    def __init__(self, log_directory: str = "./audit"):
        self.log_directory = log_directory
        self.render_window = deque(maxlen=100)
        
    def record_inject_event(self, session_id: str, snapshot_uuid: str, success: bool, latency_ms: float) -> None:
        log_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "sessionId": session_id,
            "snapshotUUID": snapshot_uuid,
            "success": success,
            "latencyMs": latency_ms,
            "complianceCheck": "pii_masked" if success else "verification_failed"
        }
        
        self.render_window.append(success)
        
        # Write to append-only audit log
        log_file = f"{self.log_directory}/inject_audit_{datetime.now():%Y%m%d}.log"
        with open(log_file, "a", encoding="utf-8") as f:
            f.write(str(log_entry) + "\n")
            
        logger.info("Audit log recorded: %s", log_entry)
        
    def get_render_success_rate(self) -> float:
        if not self.render_window:
            return 0.0
        return sum(self.render_window) / len(self.render_window)

This logger maintains a rolling success rate and writes immutable audit entries for co-browse governance. The complianceCheck field explicitly documents PII masking status for compliance reviews.

Complete Working Example

The following script combines authentication, payload construction, validation, atomic injection, webhook synchronization, and audit logging into a single runnable module. Replace the environment variables and session ID before execution.

import os
import sys
import logging
from genesyscloud.platformclient.v2 import PlatformClient

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("agentassist.co_browse")

def main():
    # 1. Initialize client
    platform_client = init_genesys_client()
    
    session_id = os.environ.get("AGENT_ASSIST_SESSION_ID")
    if not session_id:
        raise ValueError("AGENT_ASSIST_SESSION_ID environment variable is required")
        
    webhook_url = os.environ.get("SYNC_WEBHOOK_URL", "https://webhook.example.com/agentassist/sync")
    
    # 2. Define snapshot parameters
    image_url = "https://storage.example.com/snapshots/secure_view.png"
    coordinate_matrix = [[100, 200], [400, 200], [400, 400], [100, 400]]
    redaction_rules = [{"x": 120, "y": 45, "width": 180, "height": 25}]
    
    # 3. Build and validate payload
    try:
        payload = build_inject_payload(session_id, image_url, coordinate_matrix, redaction_rules)
    except ValueError as err:
        logger.error("Payload validation failed: %s", err)
        sys.exit(1)
        
    # 4. Verify PII masking and resolution
    if not verify_pii_masking(image_url, redaction_rules):
        logger.error("PII masking verification failed. Aborting injection.")
        sys.exit(1)
        
    _, _ = verify_resolution_scaling(image_url, 300, 200)
    
    # 5. Initialize audit logger
    audit = AuditLogger()
    
    # 6. Execute atomic injection
    try:
        result = inject_snapshot_atomic(platform_client, session_id, payload, webhook_url)
        audit.record_inject_event(
            session_id=session_id,
            snapshot_uuid=payload["metadata"]["snapshotUUID"],
            success=True,
            latency_ms=result["latency_ms"]
        )
        logger.info("Injection complete. Render success rate: %.2f%%", audit.get_render_success_rate() * 100)
    except Exception as err:
        audit.record_inject_event(
            session_id=session_id,
            snapshot_uuid=payload["metadata"]["snapshotUUID"],
            success=False,
            latency_ms=0.0
        )
        logger.error("Injection pipeline failed: %s", err)
        sys.exit(1)

if __name__ == "__main__":
    main()

Run the script with python co_browse_injector.py after setting GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, AGENT_ASSIST_SESSION_ID, and optionally SYNC_WEBHOOK_URL. The module validates schemas, enforces frequency limits, verifies PII coverage, injects atomically, synchronizes via webhook, and writes audit logs.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired or invalid OAuth token, missing agentassist:guide:write scope, or incorrect client credentials.
• Fix: Verify the OAuth client configuration in Genesys Cloud admin. Ensure the scope agentassist:guide:write is attached to the client. The SDK automatically refreshes tokens, but manual token cache invalidation may require restarting the process.
• Code fix: Add explicit scope validation during initialization:

if "agentassist:guide:write" not in platform_client.auth_client.get_scopes():
    raise PermissionError("Missing required scope: agentassist:guide:write")

Error: 403 Forbidden

• Cause: The OAuth client lacks permissions for the target organization, or the session ID belongs to a different tenant.
• Fix: Confirm the session ID matches the authenticated tenant. Verify the OAuth client has agentassist:session:write and agentassist:guide:write scopes enabled in the security profile.

Error: 400 Bad Request (Schema Validation Failure)

• Cause: Coordinate matrix dimensions do not match the 4x2 requirement, image format is unsupported, or redaction directives contain negative values.
• Fix: Validate payloads using the SnapshotInjectPayload Pydantic model before transmission. Check that position.width and position.height are positive integers.
• Code fix: Wrap the SDK call in a try-except block that parses the error body:

except Exception as err:
    if "400" in str(err):
        logger.error("Schema validation failed: %s", str(err))
    raise

Error: 429 Too Many Requests

• Cause: Exceeding the maximum snapshot frequency limit or hitting tenant-level rate caps.
• Fix: The inject_snapshot_atomic function implements exponential backoff. Increase the MAX_FREQUENCY_HZ threshold cautiously. Monitor the render_success_rate metric to adjust injection cadence dynamically.

Error: 500 Internal Server Error

• Cause: Temporary Genesys Cloud platform outage or image URL accessibility failure.
• Fix: Verify the image_url is publicly accessible or authenticated via signed URL. Retry with a longer backoff interval. Log the event for platform support escalation if the error persists beyond 60 seconds.

Official References

• Genesys Cloud Agent Assist API Documentation
• Agent Assist Guide Injection Endpoint
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow
• Agent Assist Scopes and Permissions