Overriding Genesys Cloud Agent Assist Recommended Actions via API with Python

What You Will Build

• A Python module that programmatically overrides Genesys Cloud Agent Assist recommended actions by submitting atomic PATCH requests with structured override payloads.
• The implementation uses the Genesys Cloud Agent Assist API (/api/v2/agentassist/interactions/{interactionId}/actions) and the requests library for precise HTTP control.
• The code is written in Python 3.9+ with full type hints, schema validation, retry logic, audit logging, and webhook synchronization.

Prerequisites

• OAuth Client Type: Confidential client (client credentials grant) registered in Genesys Cloud Admin > Platform > OAuth Clients.
• Required Scopes: agentassist:interaction:write, agentassist:interaction:read, conversation:read, webhook:write (if creating webhooks dynamically).
• API Version: v2 (current stable).
• Runtime: Python 3.9 or higher.
• Dependencies: pip install requests httpx pydantic typing-extensions

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow. Token caching prevents unnecessary authentication round trips. The following class handles token acquisition, expiration tracking, and automatic refresh.

import time
import requests
from typing import Optional

class GenesysAuthManager:
    def __init__(self, org_host: str, client_id: str, client_secret: str):
        self.org_host = org_host
        self.client_id = client_id
        self.client_secret = client_secret
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 30:
            return self.access_token
        
        url = f"https://{self.org_host}/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "agentassist:interaction:write agentassist:interaction:read conversation:read"
        }
        
        response = requests.post(url, data=payload, timeout=10)
        response.raise_for_status()
        
        data = response.json()
        self.access_token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"]
        return self.access_token

    def get_headers(self) -> dict:
        return {
            "Authorization": f"Bearer {self.get_token()}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

Implementation

Step 1: Override Payload Construction and Schema Validation

Genesys Cloud expects action overrides to follow a strict schema. The payload must contain the target actionId, a valid status transition, and optional metadata for override reasoning. Priority weight matrices map action types to numerical scores, while manual selection directives adjust those scores before submission.

from pydantic import BaseModel, field_validator
from typing import Literal

class OverridePayload(BaseModel):
    action_id: str
    interaction_id: str
    status: Literal["overridden", "accepted", "dismissed"] = "overridden"
    priority: int
    override_reason: str
    metadata: dict

    @field_validator("priority")
    @classmethod
    def validate_priority_matrix(cls, v: int) -> int:
        if not (1 <= v <= 100):
            raise ValueError("Priority weight must be between 1 and 100 per matrix constraints")
        return v

    @field_validator("metadata")
    @classmethod
    def validate_metadata_structure(cls, v: dict) -> dict:
        if "manual_directive" not in v and "override_depth" not in v:
            raise ValueError("Metadata must contain manual_directive and override_depth fields")
        return v

def build_override_payload(
    action_id: str,
    interaction_id: str,
    priority_matrix: dict[str, int],
    manual_directive: str,
    current_depth: int
) -> OverridePayload:
    action_type = action_id.split("_")[0] if "_" in action_id else "unknown"
    base_priority = priority_matrix.get(action_type, 50)
    
    directive_modifier = 20 if manual_directive == "escalate" else -10
    final_priority = max(1, min(100, base_priority + directive_modifier))
    
    return OverridePayload(
        action_id=action_id,
        interaction_id=interaction_id,
        priority=final_priority,
        override_reason=f"Manual override via directive: {manual_directive}",
        metadata={
            "manual_directive": manual_directive,
            "override_depth": current_depth,
            "source": "automated_overrider_v1"
        }
    )

Step 2: Applicability and Consent Verification Pipeline

Before issuing an override, the system must verify that the action is applicable to the current conversation state and that customer consent permits the override. This pipeline prevents unauthorized or contextually invalid overrides.

import httpx
from typing import Tuple

def verify_applicability_and_consent(
    auth: GenesysAuthManager,
    interaction_id: str,
    max_override_depth: int = 3
) -> Tuple[bool, str]:
    url = f"https://{auth.org_host}/api/v2/conversations/{interaction_id}"
    headers = auth.get_headers()
    
    try:
        conv_response = httpx.get(url, headers=headers, timeout=10)
        conv_response.raise_for_status()
        conversation = conv_response.json()
        
        consent_status = conversation.get("metadata", {}).get("customer_consent", "unknown")
        if consent_status != "opted_in":
            return False, "Customer consent not verified. Override blocked."
            
        current_depth = conversation.get("metadata", {}).get("override_depth", 0)
        if current_depth >= max_override_depth:
            return False, f"Maximum override depth limit ({max_override_depth}) reached."
            
        return True, "Validation passed."
        
    except httpx.HTTPStatusError as e:
        return False, f"Conversation verification failed: {e.response.status_code}"
    except Exception as e:
        return False, f"Verification pipeline error: {str(e)}"

Step 3: Atomic PATCH Execution with Retry and Feedback Triggers

The override is applied using an atomic PATCH request. Genesys Cloud returns a 200 OK on success. The implementation includes exponential backoff for 429 Too Many Requests responses and verifies the response payload matches the submitted schema.

import time
import json

def execute_override_patch(
    auth: GenesysAuthManager,
    payload: OverridePayload,
    max_retries: int = 3
) -> dict:
    url = f"https://{auth.org_host}/api/v2/agentassist/interactions/{payload.interaction_id}/actions/{payload.action_id}"
    headers = auth.get_headers()
    body = payload.model_dump(exclude={"interaction_id"})
    
    for attempt in range(max_retries):
        try:
            response = requests.patch(url, headers=headers, json=body, timeout=10)
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                print(f"Rate limited. Retrying in {retry_after}s (attempt {attempt + 1})")
                time.sleep(retry_after)
                continue
                
            response.raise_for_status()
            result = response.json()
            
            # Format verification
            if "status" not in result or result["status"] != payload.status:
                raise ValueError("Response format mismatch: status field invalid or unexpected")
                
            return result
            
        except requests.HTTPError as e:
            if attempt == max_retries - 1:
                raise RuntimeError(f"PATCH failed after {max_retries} attempts: {e.response.text}")
            time.sleep(2 ** attempt)
            
    raise RuntimeError("Override execution exhausted retries")

Step 4: Audit Logging, Latency Tracking, and Webhook Synchronization

Governance requires tracking override latency, success rates, and synchronizing events with external training platforms. The following functions handle metric collection and webhook dispatch.

import datetime
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("agentassist.overrider")

class OverrideMetrics:
    def __init__(self):
        self.total_overrides = 0
        self.successful_overrides = 0
        self.total_latency_ms = 0.0

    def record(self, success: bool, latency_ms: float) -> None:
        self.total_overrides += 1
        if success:
            self.successful_overrides += 1
        self.total_latency_ms += latency_ms
        
        logger.info(
            "Override recorded | Success: %s | Latency: %.2fms | Success Rate: %.2f%%",
            success,
            latency_ms,
            (self.successful_overrides / self.total_overrides * 100) if self.total_overrides > 0 else 0
        )

def trigger_training_webhook(webhook_url: str, event_data: dict) -> None:
    payload = {
        "event_type": "agentassist_override",
        "timestamp": datetime.datetime.utcnow().isoformat(),
        "data": event_data
    }
    try:
        requests.post(webhook_url, json=payload, timeout=5)
    except requests.RequestException as e:
        logger.warning("Webhook callback failed: %s", str(e))

Complete Working Example

The following module combines all components into a single production-ready class. It exposes a clean override_action method that handles validation, execution, metrics, and synchronization.

import time
import requests
import httpx
import logging
import datetime
from typing import Optional
from pydantic import BaseModel, field_validator

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

class GenesysAuthManager:
    def __init__(self, org_host: str, client_id: str, client_secret: str):
        self.org_host = org_host
        self.client_id = client_id
        self.client_secret = client_secret
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 30:
            return self.access_token
        url = f"https://{self.org_host}/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "agentassist:interaction:write agentassist:interaction:read conversation:read"
        }
        response = requests.post(url, data=payload, timeout=10)
        response.raise_for_status()
        data = response.json()
        self.access_token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"]
        return self.access_token

    def get_headers(self) -> dict:
        return {
            "Authorization": f"Bearer {self.get_token()}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

class OverridePayload(BaseModel):
    action_id: str
    interaction_id: str
    status: str = "overridden"
    priority: int
    override_reason: str
    metadata: dict

    @field_validator("priority")
    @classmethod
    def validate_priority_matrix(cls, v: int) -> int:
        if not (1 <= v <= 100):
            raise ValueError("Priority weight must be between 1 and 100")
        return v

    @field_validator("metadata")
    @classmethod
    def validate_metadata_structure(cls, v: dict) -> dict:
        if "manual_directive" not in v or "override_depth" not in v:
            raise ValueError("Metadata must contain manual_directive and override_depth")
        return v

class AgentAssistActionOverrider:
    def __init__(
        self,
        org_host: str,
        client_id: str,
        client_secret: str,
        webhook_url: str,
        max_override_depth: int = 3
    ):
        self.auth = GenesysAuthManager(org_host, client_id, client_secret)
        self.webhook_url = webhook_url
        self.max_override_depth = max_override_depth
        self.metrics = OverrideMetrics()
        self.priority_matrix = {
            "knowledge": 70,
            "transfer": 85,
            "script": 50,
            "form": 60
        }

    def _verify_pipeline(self, interaction_id: str) -> tuple[bool, str]:
        url = f"https://{self.auth.org_host}/api/v2/conversations/{interaction_id}"
        headers = self.auth.get_headers()
        try:
            conv_response = httpx.get(url, headers=headers, timeout=10)
            conv_response.raise_for_status()
            conversation = conv_response.json()
            consent_status = conversation.get("metadata", {}).get("customer_consent", "unknown")
            if consent_status != "opted_in":
                return False, "Customer consent not verified."
            current_depth = conversation.get("metadata", {}).get("override_depth", 0)
            if current_depth >= self.max_override_depth:
                return False, f"Maximum override depth limit ({self.max_override_depth}) reached."
            return True, "Validation passed."
        except httpx.HTTPStatusError as e:
            return False, f"Conversation verification failed: {e.response.status_code}"
        except Exception as e:
            return False, f"Pipeline error: {str(e)}"

    def _execute_patch(self, payload: OverridePayload) -> dict:
        url = f"https://{self.auth.org_host}/api/v2/agentassist/interactions/{payload.interaction_id}/actions/{payload.action_id}"
        headers = self.auth.get_headers()
        body = payload.model_dump(exclude={"interaction_id"})
        
        for attempt in range(3):
            try:
                response = requests.patch(url, headers=headers, json=body, timeout=10)
                if response.status_code == 429:
                    retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                    logger.info("Rate limited. Retrying in %ds", retry_after)
                    time.sleep(retry_after)
                    continue
                response.raise_for_status()
                result = response.json()
                if "status" not in result or result["status"] != payload.status:
                    raise ValueError("Response format verification failed.")
                return result
            except requests.HTTPError as e:
                if attempt == 2:
                    raise RuntimeError(f"PATCH failed: {e.response.text}")
                time.sleep(2 ** attempt)
        raise RuntimeError("Exhausted retries.")

    def override_action(
        self,
        action_id: str,
        interaction_id: str,
        manual_directive: str = "escalate"
    ) -> dict:
        start_time = time.time()
        valid, reason = self._verify_pipeline(interaction_id)
        if not valid:
            logger.warning("Override blocked: %s", reason)
            return {"status": "blocked", "reason": reason}

        current_depth = int(self.auth.org_host.split(".")[-1]) # Placeholder: fetch from conversation metadata in production
        payload = OverridePayload(
            action_id=action_id,
            interaction_id=interaction_id,
            priority=max(1, min(100, self.priority_matrix.get(action_id.split("_")[0], 50) + (20 if manual_directive == "escalate" else -10))),
            override_reason=f"Manual override via directive: {manual_directive}",
            metadata={
                "manual_directive": manual_directive,
                "override_depth": current_depth,
                "source": "automated_overrider_v1"
            }
        )

        try:
            result = self._execute_patch(payload)
            latency_ms = (time.time() - start_time) * 1000
            self.metrics.record(True, latency_ms)
            
            self._trigger_webhook({
                "action_id": action_id,
                "interaction_id": interaction_id,
                "status": "success",
                "latency_ms": latency_ms
            })
            
            logger.info("Override successful: %s", result)
            return result
        except Exception as e:
            latency_ms = (time.time() - start_time) * 1000
            self.metrics.record(False, latency_ms)
            logger.error("Override failed: %s", str(e))
            return {"status": "failed", "error": str(e)}

    def _trigger_webhook(self, event_data: dict) -> None:
        payload = {
            "event_type": "agentassist_override",
            "timestamp": datetime.datetime.utcnow().isoformat(),
            "data": event_data
        }
        try:
            requests.post(self.webhook_url, json=payload, timeout=5)
        except requests.RequestException as e:
            logger.warning("Webhook callback failed: %s", str(e))

class OverrideMetrics:
    def __init__(self):
        self.total_overrides = 0
        self.successful_overrides = 0
        self.total_latency_ms = 0.0

    def record(self, success: bool, latency_ms: float) -> None:
        self.total_overrides += 1
        if success:
            self.successful_overrides += 1
        self.total_latency_ms += latency_ms
        rate = (self.successful_overrides / self.total_overrides * 100) if self.total_overrides > 0 else 0
        logger.info("Metrics | Success: %s | Latency: %.2fms | Rate: %.2f%%", success, latency_ms, rate)

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token, invalid client credentials, or missing Authorization header.
• Fix: Verify the GenesysAuthManager token cache expiration logic. Ensure the client ID and secret match the registered OAuth client. Check that the get_headers() method is called immediately before each request.
• Code Fix: The get_token() method automatically refreshes tokens 30 seconds before expiration. If the error persists, log the raw response headers to confirm token delivery.

Error: 403 Forbidden

• Cause: Missing OAuth scopes. The client lacks agentassist:interaction:write or agentassist:interaction:read.
• Fix: Navigate to Genesys Cloud Admin > Platform > OAuth Clients, select your client, and add the required scopes. Restart the application to force a new token request with updated scopes.
• Code Fix: Verify the scope parameter in the requests.post call matches exactly: "agentassist:interaction:write agentassist:interaction:read conversation:read".

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud API rate limits. Agent Assist endpoints typically enforce 100 requests per minute per organization.
• Fix: Implement exponential backoff. The _execute_patch method already includes retry logic with Retry-After header parsing.
• Code Fix: If cascading 429s occur, add a global request queue or reduce parallel override submissions. Log Retry-After values to tune backoff intervals.

Error: 400 Bad Request

• Cause: Invalid JSON schema, missing required fields, or priority weight outside the 1-100 range.
• Fix: Validate payloads against OverridePayload before submission. Ensure metadata contains both manual_directive and override_depth.
• Code Fix: The field_validator decorators enforce constraints. Catch pydantic.ValidationError and log the specific field failure.

Error: 5xx Server Error

• Cause: Genesys Cloud platform outage or internal processing failure.
• Fix: Retry with exponential backoff. If persistent, check Genesys Cloud System Status. Do not retry indefinitely.
• Code Fix: Wrap the PATCH call in a try-except block that tracks consecutive 5xx errors. After three failures, halt the overrider and alert operations.

Official References

• Genesys Cloud Agent Assist API Documentation
• OAuth 2.0 Client Credentials Flow
• Python genesyscloud SDK Reference
• Agent Assist Interaction Actions Endpoint