Executing NICE Cognigy Bot Test Suites via REST API with Python

What You Will Build

• A Python module that submits Cognigy test suite executions, validates payload constraints against execution limits, and polls asynchronous job results.
• The implementation uses the Cognigy REST API v1 endpoints for test orchestration, result retrieval, and webhook synchronization.
• The code is written in Python 3.10+ using httpx, pydantic, and asyncio for production-grade async execution.

Prerequisites

• OAuth 2.0 Client Credentials flow configured in Cognigy with scopes: test:execute, test:read, webhook:write
• Cognigy API v1 base URL (e.g., https://api.eu1.cognigy.com)
• Python 3.10 or higher
• External dependencies: httpx>=0.25.0, pydantic>=2.5.0, python-dotenv>=1.0.0
• Install dependencies: pip install httpx pydantic python-dotenv

Authentication Setup

Cognigy uses a standard OAuth 2.0 Client Credentials grant for automated service accounts. The token endpoint returns a bearer token with a default lifetime of 3600 seconds. You must implement token caching and automatic refresh before token expiration to prevent 401 interruptions during long-running test suites.

import httpx
import time
from typing import Optional

class CognigyAuth:
    def __init__(self, client_id: str, client_secret: str, base_url: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"{base_url}/api/v1/oauth/token"
        self._token: Optional[str] = None
        self._expires_at: float = 0.0

    async def get_token(self) -> str:
        if self._token and time.time() < self._expires_at - 60:
            return self._token

        async with httpx.AsyncClient(timeout=10.0) 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": "test:execute test:read webhook:write"
                }
            )
            response.raise_for_status()
            payload = response.json()
            self._token = payload["access_token"]
            self._expires_at = time.time() + payload["expires_in"]
            return self._token

Implementation

Step 1: Payload Construction and Schema Validation

Cognigy test suite executions require a structured JSON payload containing the suite identifier, test case matrix, and expected outcome directives. The platform enforces a maximum test case count of 500 per execution and a hard timeout limit of 1800 seconds. You must validate these constraints before submission to prevent 400 Bad Request responses and unnecessary API consumption.

from pydantic import BaseModel, Field, field_validator
from typing import List, Dict, Any

class TestOutcomeDirective(BaseModel):
    expected_intent: Optional[str] = None
    expected_entities: Optional[List[str]] = None
    expected_response_contains: Optional[str] = None
    allowed_latency_ms: int = Field(ge=100, le=5000)

class TestCaseMatrix(BaseModel):
    input_text: str
    context_variables: Dict[str, Any] = {}
    outcome: TestOutcomeDirective

class TestExecutionPayload(BaseModel):
    suite_id: str
    test_cases: List[TestCaseMatrix]
    execution_timeout_seconds: int = Field(ge=30, le=1800)

    @field_validator("test_cases")
    @classmethod
    def validate_case_count(cls, v: List[TestCaseMatrix]) -> List[TestCaseMatrix]:
        if len(v) > 500:
            raise ValueError("Test suite exceeds maximum allowed case count of 500.")
        return v

Step 2: Async Execution Submission and Job Polling

After validation, you submit the payload to the Cognigy execution endpoint. The API responds immediately with an execution identifier and an asynchronous job status. You must implement exponential backoff polling to handle 429 rate limits and respect the platform’s job processing queue. The polling loop continues until the status reaches completed, failed, or timeout.

import asyncio
import logging

logger = logging.getLogger(__name__)

class CognigyTestExecutor:
    def __init__(self, auth: CognigyAuth, base_url: str):
        self.auth = auth
        self.base_url = base_url
        self.client = httpx.AsyncClient(timeout=30.0)

    async def submit_execution(self, payload: TestExecutionPayload) -> str:
        token = await self.auth.get_token()
        endpoint = f"{self.base_url}/api/v1/test-suites/{payload.suite_id}/executions"
        
        response = await self.client.post(
            endpoint,
            headers={"Authorization": f"Bearer {token}"},
            json=payload.model_dump()
        )
        
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 5))
            logger.warning("Rate limited. Retrying in %d seconds.", retry_after)
            await asyncio.sleep(retry_after)
            return await self.submit_execution(payload)
            
        response.raise_for_status()
        execution_id = response.json()["execution_id"]
        logger.info("Execution submitted with ID: %s", execution_id)
        return execution_id

    async def poll_status(self, execution_id: str, max_retries: int = 60) -> Dict[str, Any]:
        token = await self.auth.get_token()
        endpoint = f"{self.base_url}/api/v1/executions/{execution_id}/status"
        
        for attempt in range(max_retries):
            response = await self.client.get(
                endpoint,
                headers={"Authorization": f"Bearer {token}"}
            )
            response.raise_for_status()
            status_data = response.json()
            
            if status_data["status"] in ("completed", "failed", "timeout"):
                return status_data
                
            await asyncio.sleep(5 + attempt)
            
        raise TimeoutError("Test execution exceeded maximum polling attempts.")

Step 3: Result Aggregation, Assertion Verification, and Coverage Analysis

Once the execution completes, you retrieve the detailed results. Cognigy returns a structured array containing assertion verification outcomes, entity extraction accuracy, and intent classification confidence scores. You must parse these results to calculate pass rates, identify false positives, and generate coverage metrics for MLOps tracking.

    async def fetch_and_validate_results(self, execution_id: str) -> Dict[str, Any]:
        token = await self.auth.get_token()
        endpoint = f"{self.base_url}/api/v1/executions/{execution_id}/results"
        
        response = await self.client.get(
            endpoint,
            headers={"Authorization": f"Bearer {token}"}
        )
        response.raise_for_status()
        results = response.json()
        
        total_cases = len(results.get("test_results", []))
        passed_cases = 0
        coverage_metrics = {"intent_coverage": 0.0, "entity_coverage": 0.0}
        
        for case in results.get("test_results", []):
            assertions = case.get("assertions", [])
            case_passed = all(a.get("passed", False) for a in assertions)
            if case_passed:
                passed_cases += 1
                if case.get("matched_intent"):
                    coverage_metrics["intent_coverage"] += 1
                if case.get("extracted_entities"):
                    coverage_metrics["entity_coverage"] += 1
        
        coverage_metrics["intent_coverage"] = (coverage_metrics["intent_coverage"] / total_cases) if total_cases > 0 else 0.0
        coverage_metrics["entity_coverage"] = (coverage_metrics["entity_coverage"] / total_cases) if total_cases > 0 else 0.0
        
        return {
            "total": total_cases,
            "passed": passed_cases,
            "pass_rate": (passed_cases / total_cases) if total_cases > 0 else 0.0,
            "coverage": coverage_metrics,
            "raw_results": results
        }

Step 4: Webhook Synchronization and CI/CD Gateway Integration

Test completion events must synchronize with external CI/CD pipelines to enforce quality gates. You post a structured payload to your configured webhook endpoint containing execution identifiers, pass rates, and coverage thresholds. The webhook handler should return a 200 OK response to acknowledge receipt and trigger downstream pipeline stages.

    async def trigger_webhook(self, execution_id: str, metrics: Dict[str, Any], webhook_url: str) -> bool:
        webhook_payload = {
            "execution_id": execution_id,
            "status": "passed" if metrics["pass_rate"] >= 0.95 else "failed",
            "pass_rate": metrics["pass_rate"],
            "coverage": metrics["coverage"],
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
        }
        
        try:
            response = await self.client.post(
                webhook_url,
                json=webhook_payload,
                headers={"Content-Type": "application/json"}
            )
            response.raise_for_status()
            logger.info("Webhook triggered successfully for execution %s", execution_id)
            return True
        except httpx.HTTPStatusError as e:
            logger.error("Webhook delivery failed: %s", e.response.text)
            return False

Step 5: Audit Logging and MLOps Efficiency Tracking

Governance compliance requires immutable audit logs for every test execution. You record submission timestamps, execution latency, assertion failures, and coverage deltas. These logs feed into MLOps dashboards to track bot regression trends and model drift over time.

import json
from datetime import datetime

    async def generate_audit_log(self, execution_id: str, metrics: Dict[str, Any], start_time: float) -> str:
        latency = time.time() - start_time
        audit_record = {
            "audit_id": f"AUD-{execution_id}-{int(time.time())}",
            "execution_id": execution_id,
            "submission_time": datetime.utcnow().isoformat(),
            "execution_latency_seconds": round(latency, 2),
            "pass_rate": metrics["pass_rate"],
            "coverage_analysis": metrics["coverage"],
            "governance_status": "compliant" if metrics["pass_rate"] >= 0.95 else "non_compliant",
            "mlops_metrics": {
                "false_positive_rate": 1.0 - metrics["pass_rate"],
                "coverage_delta": metrics["coverage"]["intent_coverage"] - 0.80
            }
        }
        
        log_entry = json.dumps(audit_record, indent=2)
        logger.info("Audit log generated: %s", log_entry)
        return log_entry

Complete Working Example

The following script combines all components into a single executable module. Replace the environment variables with your Cognigy credentials and webhook endpoint before execution.

import asyncio
import os
import logging
from dotenv import load_dotenv

load_dotenv()

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

async def run_test_suite():
    base_url = os.getenv("COGNIGY_API_URL", "https://api.eu1.cognigy.com")
    client_id = os.getenv("COGNIGY_CLIENT_ID")
    client_secret = os.getenv("COGNIGY_CLIENT_SECRET")
    webhook_url = os.getenv("CI_WEBHOOK_URL")
    suite_id = os.getenv("COGNIGY_SUITE_ID", "suite_prod_v2")

    auth = CognigyAuth(client_id, client_secret, base_url)
    executor = CognigyTestExecutor(auth, base_url)

    payload = TestExecutionPayload(
        suite_id=suite_id,
        execution_timeout_seconds=900,
        test_cases=[
            TestCaseMatrix(
                input_text="I need to reset my password",
                context_variables={"user_role": "customer"},
                outcome=TestOutcomeDirective(
                    expected_intent="intent_password_reset",
                    expected_entities=["user_id"],
                    expected_response_contains="password reset link",
                    allowed_latency_ms=800
                )
            ),
            TestCaseMatrix(
                input_text="What are your business hours?",
                context_variables={},
                outcome=TestOutcomeDirective(
                    expected_intent="intent_business_hours",
                    expected_response_contains="open from",
                    allowed_latency_ms=500
                )
            )
        ]
    )

    start_time = time.time()
    execution_id = await executor.submit_execution(payload)
    status = await executor.poll_status(execution_id)
    metrics = await executor.fetch_and_validate_results(execution_id)
    
    await executor.trigger_webhook(execution_id, metrics, webhook_url)
    audit_log = await executor.generate_audit_log(execution_id, metrics, start_time)
    
    print(f"Execution {execution_id} completed. Pass rate: {metrics['pass_rate']:.2%}")
    print(f"Audit ID: {audit_log.split('AUD-')[1].split('\"')[0]}")

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

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token expired during the polling phase or the client credentials lack the required scopes.
• Fix: Ensure the CognigyAuth class refreshes tokens 60 seconds before expiration. Verify the scope string includes test:execute test:read webhook:write.
• Code Fix: The get_token method already implements time-based caching. If you encounter intermittent 401s, reduce the cache buffer to 30 seconds.

Error: 400 Bad Request (Payload Validation)

• Cause: The test case matrix exceeds 500 entries, the timeout exceeds 1800 seconds, or required directive fields are missing.
• Fix: Run the payload through the TestExecutionPayload Pydantic model before submission. The field_validator catches count violations automatically.
• Code Fix: Add explicit schema validation in your CI pipeline before calling the executor.

Error: 429 Too Many Requests

• Cause: Cognigy enforces strict rate limits on execution submissions and status polling. Concurrent pipeline runs trigger cascading throttling.
• Fix: Implement exponential backoff with jitter. The submit_execution method reads the Retry-After header and sleeps accordingly.
• Code Fix: Increase the initial sleep interval in poll_status to 8 seconds if running multiple suites in parallel.

Error: 504 Gateway Timeout

• Cause: The test suite execution exceeds the platform hard limit or the polling loop terminates prematurely.
• Fix: Break large suites into smaller batches under 200 cases. Increase max_retries in poll_status for complex NLP validation workflows.
• Code Fix: Add a circuit breaker pattern that aborts execution if latency exceeds 1500 seconds.

Error: Webhook Delivery Failure

• Cause: The CI/CD gateway rejects the payload due to missing authentication headers or malformed JSON.
• Fix: Verify the webhook endpoint accepts POST requests with application/json content type. Add retry logic with idempotency keys to prevent duplicate pipeline triggers.
• Code Fix: Wrap the webhook call in a retry decorator that attempts delivery three times with exponential delay.

Official References

• Cognigy API v1 Test Execution Documentation
• Cognigy OAuth 2.0 Client Credentials Flow
• httpx Async Client Reference
• Pydantic Field Validation Guide