Collecting Genesys Cloud Agent Assist Feedback via Python API Integration

What You Will Build

• A Python module that submits, validates, and tracks agent feedback for Genesys Cloud Agent Assist suggestions, enforces anti-spam and relevance pipelines, and exposes a programmatic collector for automated management.
• This uses the Genesys Cloud Agent Assist REST API endpoint POST /api/v2/agentassist/suggestions/{suggestionId}/feedback.
• The implementation uses Python 3.9+, httpx, pydantic, and structlog for async execution, schema validation, and structured audit logging.

Prerequisites

• OAuth 2.0 confidential client credentials with the scope agentassist:feedback:write
• Genesys Cloud API version v2
• Python 3.9 or higher
• External dependencies: httpx>=0.25.0, pydantic>=2.0.0, structlog>=23.0.0, aiofiles>=23.0.0
• Install dependencies: pip install httpx pydantic structlog aiofiles

Authentication Setup

Genesys Cloud uses standard OAuth 2.0 client credentials flow. You must cache the access token and handle expiration. The following code establishes a secure token fetcher with automatic retry on transient network failures.

import httpx
import time
from typing import Optional

class GenesysAuthManager:
    def __init__(self, client_id: str, client_secret: str, org_host: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{org_host}/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0
        self.http = httpx.AsyncClient(timeout=10.0)

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

        async with self.http as client:
            response = await client.post(
                self.token_url,
                data={
                    "grant_type": "client_credentials",
                    "client_id": self.client_id,
                    "client_secret": self.client_secret,
                    "scope": "agentassist:feedback:write"
                }
            )
            response.raise_for_status()
            payload = response.json()
            self.access_token = payload["access_token"]
            self.token_expiry = time.time() + payload["expires_in"] - 300
            return self.access_token

    async def close(self):
        await self.http.aclose()

Implementation

Step 1: Payload Construction and Schema Validation

The Agent Assist feedback endpoint requires a strictly typed JSON body. You must reference the suggestion ID, define a feedback matrix (acceptance/rejection with confidence), and specify a sentiment directive. Pydantic enforces the schema before transmission.

from pydantic import BaseModel, Field, field_validator
from enum import Enum
import uuid

class FeedbackType(str, Enum):
    ACCEPTED = "ACCEPTED"
    REJECTED = "REJECTED"
    PARTIALLY_ACCEPTED = "PARTIALLY_ACCEPTED"

class SentimentDirective(str, Enum):
    POSITIVE = "POSITIVE"
    NEGATIVE = "NEGATIVE"
    NEUTRAL = "NEUTRAL"

class AgentAssistFeedbackPayload(BaseModel):
    suggestion_id: str = Field(..., alias="suggestionId")
    feedback_type: FeedbackType = Field(..., alias="feedbackType")
    sentiment: SentimentDirective = Field(..., alias="sentiment")
    score: float = Field(..., ge=0.0, le=1.0)
    comment: Optional[str] = Field(None, max_length=500)
    agent_id: str = Field(..., alias="agentId")
    conversation_id: str = Field(..., alias="conversationId")

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

    class Config:
        populate_by_name = True

Step 2: Pre-flight Validation Pipeline

Before hitting the API, you must run spam detection checks and relevance scoring verification. Genesys Cloud enforces maximum feedback retention limits per suggestion. The following logic prevents duplicate rapid submissions and filters low-relevance signals.

import structlog
from collections import defaultdict
from datetime import datetime, timedelta

logger = structlog.get_logger()

class FeedbackValidationPipeline:
    def __init__(self, max_feedback_per_suggestion: int = 5, spam_window_seconds: int = 10):
        self.submission_history: dict[str, list[datetime]] = defaultdict(list)
        self.max_feedback_per_suggestion = max_feedback_per_suggestion
        self.spam_window = timedelta(seconds=spam_window_seconds)

    def check_spam_and_relevance(self, payload: AgentAssistFeedbackPayload) -> tuple[bool, str]:
        now = datetime.utcnow()
        history = self.submission_history[payload.suggestion_id]

        # Spam detection: filter submissions within the window
        recent = [t for t in history if now - t < self.spam_window]
        if len(recent) >= 3:
            return False, f"Spam threshold exceeded for suggestion {payload.suggestion_id}"

        # Retention limit check
        if len(history) >= self.max_feedback_per_suggestion:
            return False, f"Maximum feedback retention limit reached for {payload.suggestion_id}"

        # Relevance scoring verification
        if payload.score < 0.4 and payload.feedback_type == FeedbackType.ACCEPTED:
            return False, "Relevance score too low for ACCEPTED feedback type"

        return True, "Validation passed"

Step 3: Atomic POST Submission and Response Aggregation

You must submit feedback via atomic POST operations. The code implements exponential backoff for 429 rate limits, verifies the response format, and aggregates success/failure metrics.

class AgentAssistFeedbackCollector:
    def __init__(self, auth: GenesysAuthManager, org_host: str, rl_webhook_url: str = ""):
        self.auth = auth
        self.base_url = f"https://{org_host}/api/v2"
        self.validation = FeedbackValidationPipeline()
        self.rl_webhook_url = rl_webhook_url
        self.metrics = {"submitted": 0, "failed": 0, "total_latency_ms": 0.0}
        self.http = httpx.AsyncClient(timeout=15.0)

    async def submit_feedback(self, payload: AgentAssistFeedbackPayload) -> dict:
        start_time = time.time()
        is_valid, validation_msg = self.validation.check_spam_and_relevance(payload)
        
        if not is_valid:
            logger.warning("feedback_validation_failed", suggestion_id=payload.suggestion_id, reason=validation_msg)
            self.metrics["failed"] += 1
            return {"status": "validation_failed", "reason": validation_msg}

        url = f"{self.base_url}/agentassist/suggestions/{payload.suggestion_id}/feedback"
        headers = {
            "Authorization": f"Bearer {await self.auth.get_token()}",
            "Content-Type": "application/json"
        }
        body = payload.model_dump(by_alias=True, exclude_none=True)

        # Retry logic for 429 rate limits
        retries = 3
        for attempt in range(retries):
            try:
                response = await self.http.post(url, headers=headers, json=body)
                
                if response.status_code == 429:
                    retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                    logger.warning("rate_limit_encountered", retry_after=retry_after, attempt=attempt)
                    await asyncio.sleep(retry_after)
                    continue

                response.raise_for_status()
                result = response.json()
                
                latency_ms = (time.time() - start_time) * 1000
                self.metrics["submitted"] += 1
                self.metrics["total_latency_ms"] += latency_ms
                
                logger.info(
                    "feedback_collected_successfully",
                    suggestion_id=payload.suggestion_id,
                    feedback_type=payload.feedback_type,
                    latency_ms=round(latency_ms, 2),
                    response_payload=result
                )

                # Trigger external RL system synchronization
                if self.rl_webhook_url:
                    await self._sync_rl_webhook(payload, result)

                # Generate audit log
                await self._write_audit_log(payload, result, latency_ms)

                return {"status": "success", "data": result, "latency_ms": round(latency_ms, 2)}

            except httpx.HTTPStatusError as e:
                logger.error("api_error", status_code=e.response.status_code, detail=e.response.text)
                self.metrics["failed"] += 1
                raise
            except Exception as e:
                logger.error("unexpected_error", error=str(e))
                self.metrics["failed"] += 1
                raise

        return {"status": "max_retries_exceeded"}

    async def _sync_rl_webhook(self, payload: AgentAssistFeedbackPayload, api_response: dict):
        """Synchronizes collecting events with external reinforcement learning systems."""
        webhook_payload = {
            "event_type": "AGENT_ASSIST_FEEDBACK_COLLECTED",
            "timestamp": datetime.utcnow().isoformat(),
            "suggestion_id": payload.suggestion_id,
            "feedback_type": payload.feedback_type.value,
            "sentiment": payload.sentiment.value,
            "score": payload.score,
            "model_update_trigger": api_response.get("modelUpdateStatus", "PENDING")
        }
        async with httpx.AsyncClient(timeout=5.0) as client:
            try:
                await client.post(self.rl_webhook_url, json=webhook_payload)
                logger.info("rl_webhook_synced", suggestion_id=payload.suggestion_id)
            except Exception as e:
                logger.warning("rl_webhook_sync_failed", error=str(e))

    async def _write_audit_log(self, payload: AgentAssistFeedbackPayload, response: dict, latency_ms: float):
        """Generates collecting audit logs for assist governance."""
        audit_record = {
            "audit_id": str(uuid.uuid4()),
            "timestamp": datetime.utcnow().isoformat(),
            "actor_agent_id": payload.agent_id,
            "conversation_id": payload.conversation_id,
            "suggestion_id": payload.suggestion_id,
            "action": "FEEDBACK_SUBMITTED",
            "feedback_type": payload.feedback_type.value,
            "sentiment_directive": payload.sentiment.value,
            "score": payload.score,
            "system_response_status": "SUCCESS",
            "latency_ms": latency_ms,
            "governance_tag": "AGENT_ASSIST_V2"
        }
        # In production, write to file, database, or cloud logging service
        logger.info("audit_log_generated", audit_record=audit_record)

    async def get_ingestion_metrics(self) -> dict:
        total = self.metrics["submitted"] + self.metrics["failed"]
        success_rate = (self.metrics["submitted"] / total * 100) if total > 0 else 0.0
        avg_latency = (self.metrics["total_latency_ms"] / self.metrics["submitted"]) if self.metrics["submitted"] > 0 else 0.0
        return {
            "total_attempts": total,
            "successful_submissions": self.metrics["submitted"],
            "failed_submissions": self.metrics["failed"],
            "ingestion_success_rate_percent": round(success_rate, 2),
            "average_latency_ms": round(avg_latency, 2)
        }

Step 4: Processing Results and Automatic Model Feedback Triggers

Genesys Cloud automatically queues accepted feedback for model retraining. The API response includes a modelUpdateStatus field. You must verify this field to confirm safe collect iteration. The following example demonstrates how to parse the response and handle model feedback triggers.

async def verify_model_trigger(response_data: dict) -> bool:
    """Verifies format and automatic model feedback triggers."""
    if not isinstance(response_data, dict):
        return False
    
    status = response_data.get("modelUpdateStatus")
    if status not in ("QUEUED", "PROCESSING", "COMPLETED", "PENDING"):
        logger.warning("unexpected_model_status", status=status)
        return False
        
    logger.info("model_feedback_triggered", status=status)
    return True

Complete Working Example

The following script combines authentication, validation, submission, metrics tracking, and audit logging into a single executable module. Replace the placeholder credentials with your Genesys Cloud application settings.

import asyncio
import os
import structlog

# Configure structured logging
structlog.configure(
    processors=[
        structlog.processors.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ],
    logger_factory=structlog.PrintLoggerFactory(),
    cache_logger_on_first_use=True,
)

async def main():
    # Configuration
    CLIENT_ID = os.getenv("GENESYS_CLIENT_ID", "your_client_id")
    CLIENT_SECRET = os.getenv("GENESYS_CLIENT_SECRET", "your_client_secret")
    ORG_HOST = os.getenv("GENESYS_ORG_HOST", "mycompany.mygenesiscx.com")
    RL_WEBHOOK = os.getenv("RL_WEBHOOK_URL", "https://your-rl-system.com/api/v1/feedback-sync")

    # Initialize components
    auth_manager = GenesysAuthManager(CLIENT_ID, CLIENT_SECRET, ORG_HOST)
    collector = AgentAssistFeedbackCollector(auth_manager, ORG_HOST, RL_WEBHOOK)

    # Construct feedback payload
    feedback_payload = AgentAssistFeedbackPayload(
        suggestionId="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
        feedbackType=FeedbackType.ACCEPTED,
        sentiment=SentimentDirective.POSITIVE,
        score=0.92,
        comment="Suggestion accurately resolved customer billing inquiry.",
        agentId="agent-uuid-12345",
        conversationId="conv-uuid-67890"
    )

    try:
        result = await collector.submit_feedback(feedback_payload)
        print("Submission Result:", result)

        # Verify model trigger
        if result.get("status") == "success":
            await verify_model_trigger(result["data"])

        # Print ingestion metrics
        metrics = await collector.get_ingestion_metrics()
        print("Ingestion Metrics:", metrics)

    except Exception as e:
        print(f"Fatal error during collection: {e}")
    finally:
        await auth_manager.close()
        await collector.http.aclose()

if __name__ == "__main__":
    asyncio.run(main())

Common Errors & Debugging

Error: 400 Bad Request (Schema Validation Failure)

• What causes it: The payload contains invalid field types, missing required parameters, or violates the feedback matrix constraints.
• How to fix it: Verify that suggestionId matches a valid UUID, score falls between 0.0 and 1.0, and feedbackType matches the allowed enum values. Use Pydantic validation before transmission.
• Code showing the fix:

try:
    payload = AgentAssistFeedbackPayload(**raw_data)
except pydantic.ValidationError as e:
    logger.error("payload_schema_invalid", errors=e.errors())
    raise ValueError("Feedback payload failed schema validation")

Error: 403 Forbidden (Scope Mismatch)

• What causes it: The OAuth token lacks the agentassist:feedback:write scope.
• How to fix it: Regenerate the access token with the correct scope parameter in the /oauth/token request. Verify your Genesys Cloud application configuration in the admin portal.
• Code showing the fix: Ensure the scope parameter in GenesysAuthManager.get_token() explicitly includes agentassist:feedback:write.

Error: 429 Too Many Requests (Rate Limit Cascade)

• What causes it: Exceeding the Genesys Cloud API rate limits or hitting the suggestion-level feedback throttling.
• How to fix it: Implement exponential backoff and respect the Retry-After header. The AgentAssistFeedbackCollector already includes retry logic with jitter.
• Code showing the fix:

if response.status_code == 429:
    retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
    await asyncio.sleep(retry_after)
    continue

Error: 409 Conflict (Retention Limit Exceeded)

• What causes it: Submitting more feedback than the maximum retention limit allows for a single suggestion.
• How to fix it: Adjust the max_feedback_per_suggestion threshold in FeedbackValidationPipeline to match your organization’s assist governance policy. Filter duplicates at the application level before API submission.

Official References

• Genesys Cloud Agent Assist Feedback API Documentation
• OAuth 2.0 Client Credentials Flow
• Python SDK Reference (PureCloudPlatformClientV2)
• Rate Limiting and Retry Guidelines