Executing NICE Cognigy.AI Model Tests via REST APIs with Python

What You Will Build

• Automate Cognigy.AI model evaluation by submitting structured test matrices, validating payloads against engine constraints, tracking execution metrics, and synchronizing results with external test management systems.
• This tutorial uses the Cognigy AI REST API endpoints /api/v1/test-executions and /api/v1/test-executions/{executionId}/results.
• The implementation is written in Python 3.9+ using requests, pydantic, and standard library utilities.

Prerequisites

• OAuth2 Client Credentials grant configured in your Cognigy tenant
• Required scopes: models:read, test-executions:write, test-executions:read
• Python 3.9 or higher
• External dependencies: pip install requests>=2.31.0 pydantic>=2.5.0 typing-extensions>=4.8.0
• A valid Cognigy tenant base URL (format: https://<tenant>.cognigy.ai)

Authentication Setup

Cognigy uses a standard OAuth2 client credentials flow. The authentication endpoint returns a bearer token with a fixed expiration window. Production implementations must cache the token and refresh it before expiration to avoid 401 interruptions during batch test execution.

import time
import requests
from typing import Optional

class CognigyAuthManager:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.base_url = f"https://{tenant}.cognigy.ai/api/v1"
        self.client_id = client_id
        self.client_secret = client_secret
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_token(self) -> str:
        if self.token and time.time() < self.token_expiry - 60:
            return self.token

        url = f"{self.base_url}/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "models:read test-executions:write test-executions:read"
        }

        response = requests.post(url, data=payload, timeout=15)
        response.raise_for_status()
        
        data = response.json()
        self.token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"]
        
        return self.token

The get_token method enforces a sixty-second safety buffer before expiration. This prevents race conditions when multiple test execution threads request tokens simultaneously. The scope string explicitly declares the permissions required for model reading and test execution.

Implementation

Step 1: Payload Construction & Schema Validation

Cognigy enforces strict payload constraints to prevent engine overload. The test execution payload must contain a valid model identifier, a matrix of input utterances, expectation directives, and a maximum duration limit. Invalid payloads return 400 errors before the execution queue processes them.

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

class TestInput(BaseModel):
    utterance: str
    context: Dict[str, Any] = Field(default_factory=dict)
    channel: str = Field(default="web", pattern=r"^(web|sms|whatsapp|telegram)$")

class ExpectationDirective(BaseModel):
    expected_intent: str
    expected_entities: List[Dict[str, str]] = Field(default_factory=list)
    threshold_confidence: float = Field(ge=0.0, le=1.0, default=0.85)

class TestExecutionPayload(BaseModel):
    modelId: str
    testMatrix: List[TestInput] = Field(min_length=1, max_length=500)
    expectations: ExpectationDirective
    maxDurationMs: int = Field(ge=5000, le=300000)
    callbackUrl: Optional[str] = None

    @field_validator("maxDurationMs")
    @classmethod
    def validate_duration_limits(cls, v: int) -> int:
        if v > 120000:
            raise ValueError("Cognigy AI engine enforces a hard limit of 120000ms per test execution.")
        return v

    @field_validator("testMatrix")
    @classmethod
    def validate_input_schema(cls, v: List[TestInput]) -> List[TestInput]:
        for item in v:
            if len(item.utterance) > 500:
                raise ValueError("Input utterances must not exceed 500 characters.")
        return v

The TestExecutionPayload model enforces engine constraints at the Python layer before network transmission. The maxDurationMs validator caps requests at 120 seconds to align with Cognigy’s async execution timeout. The testMatrix validator prevents oversized payloads that trigger 413 errors. You must attach expectation directives to enable automatic threshold verification during result parsing.

Step 2: Atomic POST Execution & Format Verification

Test execution uses an atomic POST operation. Cognigy returns a 202 Accepted response with an execution identifier immediately. The actual AI inference runs asynchronously. You must implement retry logic for 429 rate limit responses, as Cognigy throttles test submissions to protect the evaluation pipeline.

import json
import time
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

class CognigyTestExecutor:
    def __init__(self, auth: CognigyAuthManager):
        self.auth = auth
        self.base_url = f"https://{auth.tenant}.cognigy.ai/api/v1"
        self.session = requests.Session()
        
        retry_strategy = Retry(
            total=3,
            backoff_factor=1.5,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["POST", "GET"]
        )
        adapter = HTTPAdapter(max_retries=retry_strategy)
        self.session.mount("https://", adapter)

    def execute_test(self, payload: TestExecutionPayload) -> str:
        token = self.auth.get_token()
        headers = {
            "Authorization": f"Bearer {token}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

        url = f"{self.base_url}/test-executions"
        json_body = payload.model_dump(by_alias=True)

        response = self.session.post(url, json=json_body, headers=headers, timeout=30)
        
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 2))
            time.sleep(retry_after)
            return self.execute_test(payload)
            
        response.raise_for_status()
        
        result = response.json()
        execution_id = result.get("executionId")
        
        if not execution_id:
            raise RuntimeError("Cognigy API returned missing executionId in 202 response.")
            
        return execution_id

The execute_test method uses requests.Session with a Retry adapter configured for 429 and 5xx status codes. The exponential backoff factor of 1.5 prevents thundering herd effects when Cognigy returns rate limit headers. The method validates the 202 response body to extract the executionId, which serves as the primary key for result aggregation. OAuth scope required: test-executions:write.

Step 3: Result Aggregation & Threshold Verification

After submission, you poll the results endpoint until the execution completes. Cognigy returns paginated result sets containing inference outputs, confidence scores, and validation flags. You must implement pagination handling and threshold verification to calculate accurate pass rates.

from typing import List, Dict, Any

class TestResultAggregator:
    def __init__(self, executor: CognigyTestExecutor):
        self.executor = executor
        self.base_url = executor.base_url

    def fetch_results(self, execution_id: str) -> List[Dict[str, Any]]:
        token = self.executor.auth.get_token()
        headers = {
            "Authorization": f"Bearer {token}",
            "Accept": "application/json"
        }

        all_results = []
        page = 1
        page_size = 100

        while True:
            url = f"{self.base_url}/test-executions/{execution_id}/results"
            params = {"page": page, "pageSize": page_size}
            
            response = requests.get(url, headers=headers, params=params, timeout=20)
            response.raise_for_status()
            
            data = response.json()
            results = data.get("results", [])
            all_results.extend(results)
            
            if len(results) < page_size or not data.get("hasMore", False):
                break
                
            page += 1
            time.sleep(0.5)
            
        return all_results

    def verify_thresholds(self, results: List[Dict[str, Any]], threshold: float) -> Dict[str, Any]:
        total = len(results)
        passed = 0
        latency_sum = 0.0
        failures = []

        for idx, item in enumerate(results):
            confidence = item.get("confidence", 0.0)
            elapsed = item.get("processingTimeMs", 0)
            latency_sum += elapsed
            
            if confidence >= threshold and item.get("validationStatus") == "PASS":
                passed += 1
            else:
                failures.append({
                    "index": idx,
                    "utterance": item.get("input", {}).get("utterance", "unknown"),
                    "confidence": confidence,
                    "status": item.get("validationStatus")
                })

        pass_rate = (passed / total * 100) if total > 0 else 0.0
        avg_latency = latency_sum / total if total > 0 else 0.0

        return {
            "totalTests": total,
            "passedTests": passed,
            "failedTests": total - passed,
            "passRatePercent": round(pass_rate, 2),
            "averageLatencyMs": round(avg_latency, 2),
            "failures": failures
        }

The fetch_results method handles pagination by tracking the hasMore flag and incrementing the page counter. The verify_thresholds method iterates through the result set, comparing confidence scores against the provided threshold and aggregating latency metrics. This pipeline prevents false positives by requiring both a confidence threshold and a validationStatus of PASS. OAuth scope required: test-executions:read.

Step 4: Callback Synchronization & Audit Logging

Production test runners synchronize with external test management tools via webhook callbacks. Cognigy invokes the callbackUrl provided in the payload when execution completes. You must implement a lightweight HTTP handler to receive these events, log audit trails, and update external systems.

import logging
import datetime
from flask import Flask, request, jsonify

app = Flask(__name__)
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("cognigy_test_executor")

@app.route("/webhooks/cognigy-test-completion", methods=["POST"])
def handle_cognigy_callback():
    payload = request.get_json()
    execution_id = payload.get("executionId")
    status = payload.get("status")
    timestamp = datetime.datetime.utcnow().isoformat()

    logger.info("Received Cognigy callback | executionId: %s | status: %s", execution_id, status)

    audit_record = {
        "timestamp": timestamp,
        "executionId": execution_id,
        "status": status,
        "source": "cognigy_ai_webhook",
        "governance_flag": "COMPLIANT" if status == "COMPLETED" else "REVIEW_REQUIRED"
    }

    with open("audit_log.jsonl", "a") as f:
        f.write(json.dumps(audit_record) + "\n")

    if status != "COMPLETED":
        logger.warning("Execution failed or timed out. Triggering external test management sync.")
        # Integration point: POST to Jira, Azure DevOps, or custom test dashboard

    return jsonify({"received": True}), 200

The callback handler validates the execution status and writes a structured audit record to a newline-delimited JSON file. This satisfies model governance requirements by creating an immutable execution trail. The handler returns a 200 response immediately to acknowledge the webhook and prevent Cognigy from retrying the delivery. You can extend the handler to push metrics to Prometheus or update CI/CD pipelines.

Complete Working Example

The following script combines authentication, payload validation, execution, result aggregation, and audit logging into a single runnable module. Replace the placeholder credentials and tenant identifier before execution.

import time
import json
import requests
from typing import Optional, List, Dict, Any
from pydantic import BaseModel, Field, field_validator
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

class CognigyAuthManager:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.tenant = tenant
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = f"https://{tenant}.cognigy.ai/api/v1"
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_token(self) -> str:
        if self.token and time.time() < self.token_expiry - 60:
            return self.token
        url = f"{self.base_url}/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "models:read test-executions:write test-executions:read"
        }
        response = requests.post(url, data=payload, timeout=15)
        response.raise_for_status()
        data = response.json()
        self.token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"]
        return self.token

class TestInput(BaseModel):
    utterance: str
    context: Dict[str, Any] = Field(default_factory=dict)
    channel: str = Field(default="web", pattern=r"^(web|sms|whatsapp|telegram)$")

class ExpectationDirective(BaseModel):
    expected_intent: str
    expected_entities: List[Dict[str, str]] = Field(default_factory=list)
    threshold_confidence: float = Field(ge=0.0, le=1.0, default=0.85)

class TestExecutionPayload(BaseModel):
    modelId: str
    testMatrix: List[TestInput] = Field(min_length=1, max_length=500)
    expectations: ExpectationDirective
    maxDurationMs: int = Field(ge=5000, le=300000)
    callbackUrl: Optional[str] = None

    @field_validator("maxDurationMs")
    @classmethod
    def validate_duration_limits(cls, v: int) -> int:
        if v > 120000:
            raise ValueError("Cognigy AI engine enforces a hard limit of 120000ms per test execution.")
        return v

class CognigyTestRunner:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.auth = CognigyAuthManager(tenant, client_id, client_secret)
        self.base_url = f"https://{tenant}.cognigy.ai/api/v1"
        self.session = requests.Session()
        retry_strategy = Retry(total=3, backoff_factor=1.5, status_forcelist=[429, 500, 502, 503, 504])
        self.session.mount("https://", HTTPAdapter(max_retries=retry_strategy))

    def run_evaluation(self, payload: TestExecutionPayload) -> Dict[str, Any]:
        token = self.auth.get_token()
        headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json", "Accept": "application/json"}
        
        execution_id = self._submit_execution(payload, headers)
        results = self._poll_results(execution_id, headers)
        metrics = self._calculate_metrics(results, payload.expectations.threshold_confidence)
        
        return {
            "executionId": execution_id,
            "metrics": metrics,
            "rawResults": results
        }

    def _submit_execution(self, payload: TestExecutionPayload, headers: Dict[str, str]) -> str:
        url = f"{self.base_url}/test-executions"
        response = self.session.post(url, json=payload.model_dump(by_alias=True), headers=headers, timeout=30)
        if response.status_code == 429:
            time.sleep(int(response.headers.get("Retry-After", 2)))
            return self._submit_execution(payload, headers)
        response.raise_for_status()
        return response.json()["executionId"]

    def _poll_results(self, execution_id: str, headers: Dict[str, str]) -> List[Dict[str, Any]]:
        url = f"{self.base_url}/test-executions/{execution_id}/results"
        all_results = []
        page = 1
        while True:
            response = self.session.get(url, headers=headers, params={"page": page, "pageSize": 100}, timeout=20)
            response.raise_for_status()
            data = response.json()
            all_results.extend(data.get("results", []))
            if not data.get("hasMore", False):
                break
            page += 1
        return all_results

    def _calculate_metrics(self, results: List[Dict[str, Any]], threshold: float) -> Dict[str, Any]:
        total = len(results)
        passed = sum(1 for r in results if r.get("confidence", 0) >= threshold and r.get("validationStatus") == "PASS")
        avg_latency = sum(r.get("processingTimeMs", 0) for r in results) / max(total, 1)
        return {"total": total, "passed": passed, "passRatePercent": round((passed / max(total, 1)) * 100, 2), "averageLatencyMs": round(avg_latency, 2)}

if __name__ == "__main__":
    runner = CognigyTestRunner(
        tenant="your-tenant",
        client_id="your-client-id",
        client_secret="your-client-secret"
    )

    test_payload = TestExecutionPayload(
        modelId="64f1a2b3c4d5e6f7a8b9c0d1",
        testMatrix=[
            TestInput(utterance="I need to reset my password", channel="web"),
            TestInput(utterance="Where is my order tracking number", context={"orderId": "ORD-9921"})
        ],
        expectations=ExpectationDirective(expected_intent="account_support", threshold_confidence=0.85),
        maxDurationMs=60000,
        callbackUrl="https://your-server.com/webhooks/cognigy-test-completion"
    )

    outcome = runner.run_evaluation(test_payload)
    print(json.dumps(outcome, indent=2))

Common Errors & Debugging

Error: 400 Bad Request

• Cause: Payload violates Cognigy engine constraints. Common triggers include maxDurationMs exceeding 120000, testMatrix containing empty utterances, or invalid channel identifiers.
• Fix: Validate the payload against the Pydantic schema before submission. Inspect the validationErrors array in the response body to identify the exact field violation.
• Code adjustment: Ensure TestExecutionPayload.model_validate() runs before the POST request.

Error: 401 Unauthorized

• Cause: Expired or invalid bearer token. The OAuth token may have expired during long-running test matrix polling.
• Fix: Implement token refresh logic with a safety buffer. The CognigyAuthManager in this tutorial refreshes tokens sixty seconds before expiration.
• Code adjustment: Verify the grant_type is client_credentials and the secret matches the Cognigy tenant configuration.

Error: 403 Forbidden

• Cause: Missing OAuth scopes. The client lacks test-executions:write or test-executions:read.
• Fix: Update the OAuth client configuration in the Cognigy admin console to include the required scopes. Regenerate the client secret if scope changes were made post-provisioning.
• Code adjustment: Confirm the scope parameter in the token request matches exactly: models:read test-executions:write test-executions:read.

Error: 429 Too Many Requests

• Cause: Rate limiting triggered by rapid test submissions or concurrent polling.
• Fix: Respect the Retry-After header. Implement exponential backoff. The HTTPAdapter with Retry handles this automatically, but manual submission loops must include time.sleep().
• Code adjustment: Monitor the X-RateLimit-Remaining header to adjust submission frequency dynamically.

Error: 500 Internal Server Error

• Cause: Cognigy AI engine timeout or model corruption. The underlying NLP pipeline failed to process the matrix.
• Fix: Reduce testMatrix size to isolate problematic utterances. Verify the modelId references a deployed, non-draft model. Retry with a fresh execution after thirty seconds.
• Code adjustment: Wrap the execution call in a try-except block that logs the executionId and falls back to manual review.

Official References

• Cognigy AI REST API Documentation
• Test Executions Endpoint Reference
• OAuth2 Authentication Guide
• Pydantic Validation Documentation
• Requests HTTP Library Documentation