Intercepting Genesys Cloud Agent Assist Transcript Chunks and Routing Them to an External LLM Gateway

What You Will Build

A Python FastAPI proxy that receives Agent Assist transcript chunk webhooks from Genesys Cloud, buffers them in an async queue, and forwards them to an external LLM gateway with strict timeout and retry controls. This uses the Genesys Cloud Agent Assist webhook mechanism and the httpx library for async HTTP calls. The tutorial covers Python 3.10+ with FastAPI, Uvicorn, and asyncio.

Prerequisites

• Genesys Cloud OAuth2 confidential client with conversation:read and analytics:query scopes (required if the proxy must call back to Genesys Cloud for metadata enrichment).
• Agent Assist skill configured to POST transcript chunks to your service endpoint.
• Python 3.10+ runtime.
• External dependencies: fastapi, uvicorn, httpx, pydantic, python-dotenv, structlog.
• An external LLM gateway endpoint accepting REST POST requests (for example, a custom proxy or OpenAI-compatible API).

Authentication Setup

Genesys Cloud Agent Assist webhooks do not carry OAuth tokens. They rely on network allowlisting and optional webhook verification tokens. However, production proxies typically require OAuth2 access to call back to Genesys Cloud APIs for conversation enrichment, annotation, or audit logging. The following implementation uses the client credentials flow with automatic token caching and refresh logic.

import time
import httpx
from typing import Optional

GENESYS_OAUTH_URL = "https://api.mypurecloud.com/oauth/token"

class GenesysTokenManager:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.environment = environment
        self.oauth_url = f"https://api.{environment}/oauth/token"
        self.access_token: Optional[str] = None
        self.expires_at: float = 0.0
        self.client = httpx.Client(timeout=10.0)

    async def get_token(self) -> str:
        if self.access_token and time.time() < self.expires_at - 30:
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "conversation:read analytics:query"
        }

        response = self.client.post(self.oauth_url, data=payload)
        response.raise_for_status()

        data = response.json()
        self.access_token = data["access_token"]
        self.expires_at = time.time() + data["expires_in"]
        return self.access_token

The get_token method caches the access token and refreshes it thirty seconds before expiration to prevent mid-request 401 failures. The scope conversation:read analytics:query permits fetching participant details and conversation history when the LLM gateway requires context enrichment.

Implementation

Step 1: Define Payload Models and Webhook Endpoint

Genesys Cloud sends transcript chunks via HTTP POST to the URL configured in the Agent Assist skill. The payload contains a conversation identifier, skill identifier, and an array of text chunks with timestamps. The FastAPI endpoint validates the payload, pushes it to an async queue, and returns an immediate 202 Accepted response to prevent Genesys Cloud from retrying the webhook.

from fastapi import FastAPI, Request, HTTPException
from pydantic import BaseModel, Field
from typing import List, Optional
import asyncio
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="Agent Assist LLM Proxy")
queue = asyncio.Queue(maxsize=150)

class TranscriptChunk(BaseModel):
    text: str = Field(..., description="Spoken or typed transcript text")
    timestamp: str = Field(..., description="ISO 8601 timestamp of the chunk")

class AgentAssistWebhookPayload(BaseModel):
    conversationId: str = Field(..., description="Genesys Cloud conversation identifier")
    skillId: str = Field(..., description="Agent Assist skill identifier")
    chunks: List[TranscriptChunk] = Field(..., description="Array of transcript segments")
    verificationToken: Optional[str] = None

@app.post("/webhook/agent-assist")
async def receive_transcript_chunks(payload: AgentAssistWebhookPayload):
    if not queue.full():
        await queue.put(payload)
        logger.info("Queued transcript chunks for conversation %s", payload.conversationId)
        return {"status": "accepted", "queued": True}
    else:
        logger.warning("Queue full. Dropping webhook for conversation %s", payload.conversationId)
        raise HTTPException(status_code=503, detail="Processing queue at capacity")

HTTP Request Cycle Example

POST /webhook/agent-assist HTTP/1.1
Host: proxy.yourdomain.com
Content-Type: application/json
X-Genesys-Webhook-Secret: your-verification-token

{
  "conversationId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "skillId": "agent-assist-skill-uuid",
  "chunks": [
    {
      "text": "Customer: I need to reset my password.",
      "timestamp": "2024-05-12T14:32:01.123Z"
    }
  ]
}

Expected Response

HTTP/1.1 202 Accepted
Content-Type: application/json

{
  "status": "accepted",
  "queued": true
}

The endpoint returns 202 immediately. Genesys Cloud interprets 2xx and 3xx as successful delivery and will not retry. A 503 response triggers Genesys Cloud webhook retry logic with exponential backoff.

Step 2: Async Queue Worker and LLM Gateway Forwarding

The background worker consumes items from the queue, formats them for the LLM gateway, and executes the HTTP POST. The worker uses httpx.AsyncClient with strict timeout configuration to prevent thread blocking.

import httpx
import json
from datetime import datetime, timezone

LLM_GATEWAY_URL = "https://llm-gateway.example.com/v1/analyze"
LLM_API_KEY = "sk-llm-proxy-key-12345"

async def llm_worker():
    async with httpx.AsyncClient(timeout=httpx.Timeout(connect=5.0, read=30.0, write=10.0, pool=5.0)) as client:
        while True:
            payload = await queue.get()
            try:
                prompt = format_prompt_for_llm(payload)
                await forward_to_llm(client, prompt, payload.conversationId)
            except Exception as e:
                logger.error("Failed to process chunks for %s: %s", payload.conversationId, str(e))
            finally:
                queue.task_done()

def format_prompt_for_llm(payload: AgentAssistWebhookPayload) -> dict:
    transcript = " ".join([chunk.text for chunk in payload.chunks])
    return {
        "model": "llm-proxy-v1",
        "messages": [
            {"role": "system", "content": "Analyze customer sentiment and extract intent."},
            {"role": "user", "content": f"Transcript: {transcript}"}
        ],
        "temperature": 0.2
    }

async def forward_to_llm(client: httpx.AsyncClient, prompt: dict, conversation_id: str):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {LLM_API_KEY}"
    }
    
    response = await client.post(
        LLM_GATEWAY_URL,
        json=prompt,
        headers=headers
    )
    response.raise_for_status()
    
    result = response.json()
    logger.info("LLM response received for %s: %s", conversation_id, result.get("id", "unknown"))
    # Add logic here to store results, call Genesys APIs, or update conversation state

The format_prompt_for_llm function flattens the transcript chunks into a single string. In production, you would preserve speaker labels and timestamps for temporal reasoning. The forward_to_llm function executes the POST request and raises an exception on non-2xx responses, which the worker catches and logs.

Step 3: Retry Logic, Timeout Handling, and Circuit Breaking

LLLM gateways frequently return 429 Too Many Requests during traffic spikes. The following implementation adds exponential backoff for 429 responses and a circuit breaker pattern for 5xx errors. Timeout exceptions are handled separately to prevent queue starvation.

import asyncio
import random

MAX_RETRIES = 3
BASE_DELAY = 1.0
CIRCUIT_BREAKER_THRESHOLD = 5
circuit_open_until = 0.0
failure_count = 0

async def forward_to_llm_with_retry(client: httpx.AsyncClient, prompt: dict, conversation_id: str):
    global circuit_open_until, failure_count
    retries = 0

    while retries <= MAX_RETRIES:
        if time.time() < circuit_open_until:
            logger.warning("Circuit breaker open. Skipping LLM call for %s", conversation_id)
            return

        try:
            headers = {
                "Content-Type": "application/json",
                "Authorization": f"Bearer {LLM_API_KEY}"
            }

            response = await client.post(
                LLM_GATEWAY_URL,
                json=prompt,
                headers=headers
            )

            if response.status_code == 429:
                retry_after = float(response.headers.get("Retry-After", BASE_DELAY * (2 ** retries)))
                logger.warning("Rate limited by LLM gateway. Retrying in %.2fs for %s", retry_after, conversation_id)
                await asyncio.sleep(retry_after)
                retries += 1
                continue

            if 500 <= response.status_code < 600:
                failure_count += 1
                if failure_count >= CIRCUIT_BREAKER_THRESHOLD:
                    circuit_open_until = time.time() + 30.0
                    logger.error("Circuit breaker tripped after %d consecutive 5xx errors", failure_count)
                raise Exception(f"LLM gateway 5xx error: {response.status_code}")

            response.raise_for_status()
            failure_count = 0
            result = response.json()
            logger.info("LLM response received for %s", conversation_id)
            return result

        except httpx.TimeoutException:
            logger.error("Timeout waiting for LLM gateway for %s", conversation_id)
            return
        except httpx.HTTPStatusError as e:
            if e.response.status_code == 401:
                logger.error("LLM gateway authentication failed for %s", conversation_id)
                return
            if e.response.status_code == 403:
                logger.error("LLM gateway permission denied for %s", conversation_id)
                return
            raise
        except Exception as e:
            logger.error("Unexpected error forwarding to LLM for %s: %s", conversation_id, str(e))
            return

The retry loop respects the Retry-After header when present. The circuit breaker halts requests for thirty seconds after five consecutive 5xx failures, preventing cascading failures. Timeout exceptions are logged and the request is discarded to maintain queue throughput. Authentication failures (401, 403) terminate the retry loop immediately since retrying will not resolve credential issues.

Complete Working Example

import time
import asyncio
import logging
import httpx
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from typing import List, Optional

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration
GENESYS_OAUTH_URL = "https://api.mypurecloud.com/oauth/token"
LLM_GATEWAY_URL = "https://llm-gateway.example.com/v1/analyze"
LLM_API_KEY = "sk-llm-proxy-key-12345"
MAX_QUEUE_SIZE = 150
MAX_RETRIES = 3
CIRCUIT_BREAKER_THRESHOLD = 5

app = FastAPI(title="Agent Assist LLM Proxy")
queue = asyncio.Queue(maxsize=MAX_QUEUE_SIZE)
circuit_open_until = 0.0
failure_count = 0

class TranscriptChunk(BaseModel):
    text: str = Field(..., description="Spoken or typed transcript text")
    timestamp: str = Field(..., description="ISO 8601 timestamp of the chunk")

class AgentAssistWebhookPayload(BaseModel):
    conversationId: str = Field(..., description="Genesys Cloud conversation identifier")
    skillId: str = Field(..., description="Agent Assist skill identifier")
    chunks: List[TranscriptChunk] = Field(..., description="Array of transcript segments")
    verificationToken: Optional[str] = None

class GenesysTokenManager:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.oauth_url = f"https://api.{environment}/oauth/token"
        self.access_token: Optional[str] = None
        self.expires_at: float = 0.0
        self.client = httpx.Client(timeout=10.0)

    async def get_token(self) -> str:
        if self.access_token and time.time() < self.expires_at - 30:
            return self.access_token
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "conversation:read analytics:query"
        }
        response = self.client.post(self.oauth_url, data=payload)
        response.raise_for_status()
        data = response.json()
        self.access_token = data["access_token"]
        self.expires_at = time.time() + data["expires_in"]
        return self.access_token

@app.post("/webhook/agent-assist")
async def receive_transcript_chunks(payload: AgentAssistWebhookPayload):
    if not queue.full():
        await queue.put(payload)
        logger.info("Queued transcript chunks for conversation %s", payload.conversationId)
        return {"status": "accepted", "queued": True}
    else:
        logger.warning("Queue full. Dropping webhook for conversation %s", payload.conversationId)
        raise HTTPException(status_code=503, detail="Processing queue at capacity")

def format_prompt_for_llm(payload: AgentAssistWebhookPayload) -> dict:
    transcript = " ".join([chunk.text for chunk in payload.chunks])
    return {
        "model": "llm-proxy-v1",
        "messages": [
            {"role": "system", "content": "Analyze customer sentiment and extract intent."},
            {"role": "user", "content": f"Transcript: {transcript}"}
        ],
        "temperature": 0.2
    }

async def forward_to_llm_with_retry(client: httpx.AsyncClient, prompt: dict, conversation_id: str):
    global circuit_open_until, failure_count
    retries = 0

    while retries <= MAX_RETRIES:
        if time.time() < circuit_open_until:
            logger.warning("Circuit breaker open. Skipping LLM call for %s", conversation_id)
            return

        try:
            headers = {
                "Content-Type": "application/json",
                "Authorization": f"Bearer {LLM_API_KEY}"
            }

            response = await client.post(
                LLM_GATEWAY_URL,
                json=prompt,
                headers=headers
            )

            if response.status_code == 429:
                retry_after = float(response.headers.get("Retry-After", 1.0 * (2 ** retries)))
                logger.warning("Rate limited by LLM gateway. Retrying in %.2fs for %s", retry_after, conversation_id)
                await asyncio.sleep(retry_after)
                retries += 1
                continue

            if 500 <= response.status_code < 600:
                failure_count += 1
                if failure_count >= CIRCUIT_BREAKER_THRESHOLD:
                    circuit_open_until = time.time() + 30.0
                    logger.error("Circuit breaker tripped after %d consecutive 5xx errors", failure_count)
                raise Exception(f"LLM gateway 5xx error: {response.status_code}")

            response.raise_for_status()
            failure_count = 0
            result = response.json()
            logger.info("LLM response received for %s", conversation_id)
            return result

        except httpx.TimeoutException:
            logger.error("Timeout waiting for LLM gateway for %s", conversation_id)
            return
        except httpx.HTTPStatusError as e:
            if e.response.status_code == 401:
                logger.error("LLM gateway authentication failed for %s", conversation_id)
                return
            if e.response.status_code == 403:
                logger.error("LLM gateway permission denied for %s", conversation_id)
                return
            raise
        except Exception as e:
            logger.error("Unexpected error forwarding to LLM for %s: %s", conversation_id, str(e))
            return

async def llm_worker():
    async with httpx.AsyncClient(timeout=httpx.Timeout(connect=5.0, read=30.0, write=10.0, pool=5.0)) as client:
        while True:
            payload = await queue.get()
            try:
                prompt = format_prompt_for_llm(payload)
                await forward_to_llm_with_retry(client, prompt, payload.conversationId)
            except Exception as e:
                logger.error("Failed to process chunks for %s: %s", payload.conversationId, str(e))
            finally:
                queue.task_done()

@app.on_event("startup")
async def startup_event():
    asyncio.create_task(llm_worker())
    logger.info("LLM worker started. Listening for Agent Assist webhooks.")

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

Run the service with python main.py. The application starts the Uvicorn server and spawns a background asyncio task that continuously drains the queue. Configure your Genesys Cloud Agent Assist skill to POST to https://your-proxy-domain/webhook/agent-assist.

Common Errors & Debugging

Error: 401 Unauthorized on Genesys Cloud OAuth Token Request

• Cause: Invalid client ID, client secret, or mismatched environment URL. The OAuth endpoint rejects credentials that do not belong to the specified subdomain.
• Fix: Verify the client credentials in the Genesys Cloud admin console under Organization > Security > OAuth Clients. Ensure the GENESYS_OAUTH_URL matches your environment domain.
• Code Check: The GenesysTokenManager raises httpx.HTTPStatusError on authentication failure. Log the raw response body to inspect the exact error message returned by the identity provider.

Error: 403 Forbidden on LLM Gateway POST

• Cause: Missing or expired API key, or the gateway restricts access by IP address or API scope.
• Fix: Rotate the LLM API key and update LLM_API_KEY in the configuration. Verify that your proxy server IP is allowlisted in the LLM gateway security settings.
• Code Check: The retry loop explicitly catches 403 responses and terminates retry attempts to avoid wasting compute on permission errors.

Error: 429 Too Many Requests with Missing Retry-After Header

• Cause: The LLM gateway enforces rate limits but omits the standard Retry-After header, or the header contains a non-numeric string.
• Fix: The fallback logic uses 1.0 * (2 ** retries) as a safe exponential backoff. If your gateway uses a custom header, update the response.headers.get() call to parse the correct field.
• Code Check: Monitor the logger.warning output to track backoff intervals. Adjust MAX_RETRIES if the gateway enforces strict daily quotas.

Error: asyncio.Queue Full (503 Response to Genesys Cloud)

• Cause: The LLM worker cannot keep pace with incoming webhook volume, causing the queue to reach MAX_QUEUE_SIZE. Genesys Cloud interprets the 503 response as a temporary failure and retries the webhook.
• Fix: Increase MAX_QUEUE_SIZE to match your expected burst capacity. Add horizontal scaling by running multiple Uvicorn workers or deploy the FastAPI service behind a message broker like RabbitMQ or AWS SQS.
• Code Check: The webhook endpoint returns 503 immediately when queue.full() evaluates to true. This prevents memory exhaustion and signals Genesys Cloud to back off gracefully.

Official References

• Genesys Cloud Agent Assist Configuration API
• Genesys Cloud OAuth2 Authentication Guide
• FastAPI Background Tasks and AsyncIO
• HTTPX Async Client Documentation
• Agent Assist Webhook Payload Schema