Simulating NICE Cognigy.AI Conversations via REST API with Python

What You Will Build

A Python module that constructs simulation payloads, validates bot versions and environment isolation, executes asynchronous conversation simulations, analyzes NLU performance metrics, and synchronizes results to external MLOps dashboards via webhooks. This uses the NICE Cognigy.AI REST API. This covers Python 3.9+ with httpx and pydantic.

Prerequisites

• OAuth2 client credentials with simulation:execute, bot:read, environment:read scopes
• Cognigy.AI API v1
• Python 3.9+
• External dependencies: httpx, pydantic, aiofiles
• Install dependencies: pip install httpx pydantic aiofiles

Authentication Setup

Cognigy.AI uses bearer token authentication. The client credentials flow exchanges your client ID and secret for an access token. You must cache the token and handle expiration before polling simulation results.

import httpx
import asyncio
from datetime import datetime, timedelta, timezone
from typing import Optional

class CognigyAuth:
    def __init__(self, client_id: str, client_secret: str, token_url: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = token_url
        self.token: Optional[str] = None
        self.expires_at: Optional[datetime] = None

    async def get_token(self) -> str:
        if self.token and self.expires_at and datetime.now(timezone.utc) < self.expires_at:
            return self.token

        async with httpx.AsyncClient() 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": "simulation:execute bot:read environment:read"
                },
                headers={"Content-Type": "application/x-www-form-urlencoded"}
            )
            response.raise_for_status()
            payload = response.json()
            self.token = payload["access_token"]
            self.expires_at = datetime.now(timezone.utc) + timedelta(seconds=payload["expires_in"] - 30)
            return self.token

Implementation

Step 1: Validate Bot Version and Environment Isolation

You must verify that the target bot version exists and that the simulation environment matches your isolation rules. Cognigy returns version metadata at /api/v1/bots/{botId}/versions and environment configuration at /api/v1/environments/{envId}.

import httpx
from pydantic import BaseModel, Field
from typing import List

class BotVersion(BaseModel):
    id: str
    name: str
    status: str
    deployed: bool

class EnvironmentRule(BaseModel):
    id: str
    name: str
    isolation_mode: str
    allowed_bot_ids: List[str]

async def validate_environment(
    base_url: str,
    token: str,
    bot_id: str,
    env_id: str,
    target_version_id: str
) -> bool:
    headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
    
    async with httpx.AsyncClient() as client:
        # Fetch bot versions
        versions_resp = await client.get(
            f"{base_url}/api/v1/bots/{bot_id}/versions",
            headers=headers
        )
        versions_resp.raise_for_status()
        versions = [BotVersion(**v) for v in versions_resp.json()]
        
        version_available = any(v.id == target_version_id and v.status == "ACTIVE" for v in versions)
        if not version_available:
            raise ValueError(f"Bot version {target_version_id} is not active or does not exist")

        # Fetch environment isolation rules
        env_resp = await client.get(
            f"{base_url}/api/v1/environments/{env_id}",
            headers=headers
        )
        env_resp.raise_for_status()
        env_data = EnvironmentRule(**env_resp.json())
        
        if env_data.isolation_mode == "STRICT" and bot_id not in env_data.allowed_bot_ids:
            raise PermissionError(f"Bot {bot_id} is blocked in environment {env_id} due to strict isolation")
            
        return True

Full HTTP Cycle Example for Step 1

GET /api/v1/bots/bot-12345/versions HTTP/1.1
Host: myenv.cognigy.ai
Authorization: Bearer eyJhbGciOiJSUzI1NiIs...
Content-Type: application/json

HTTP/1.1 200 OK
Content-Type: application/json
[
  {"id": "ver-abc", "name": "v1.2", "status": "ACTIVE", "deployed": true},
  {"id": "ver-def", "name": "v1.1", "status": "ARCHIVED", "deployed": false}
]

Step 2: Construct Simulation Payload with Assertions

The simulation payload requires sequential user inputs, initial context variables, and expected output assertions. Cognigy expects a structured JSON body. You must define intent expectations and entity extraction targets.

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

class UserInput(BaseModel):
    text: str
    delay_ms: int = 0

class Assertion(BaseModel):
    step_index: int
    expected_intent: str
    expected_confidence_min: float = 0.85
    expected_entities: Dict[str, Any] = Field(default_factory=dict)

class SimulationPayload(BaseModel):
    botId: str
    environmentId: str
    versionId: str
    inputs: List[UserInput]
    context: Dict[str, Any] = Field(default_factory=dict)
    assertions: List[Assertion]

def build_simulation_payload(
    bot_id: str,
    env_id: str,
    version_id: str,
    conversation_flow: List[str],
    expected_responses: List[Dict[str, Any]],
    initial_context: Dict[str, Any]
) -> SimulationPayload:
    inputs = [UserInput(text=msg, delay_ms=500) for msg in conversation_flow]
    assertions = [
        Assertion(
            step_index=i,
            expected_intent=resp.get("intent", ""),
            expected_confidence_min=resp.get("min_confidence", 0.8),
            expected_entities=resp.get("entities", {})
        )
        for i, resp in enumerate(expected_responses)
    ]
    return SimulationPayload(
        botId=bot_id,
        environmentId=env_id,
        versionId=version_id,
        inputs=inputs,
        context=initial_context,
        assertions=assertions
    )

Step 3: Execute Async Simulation and Poll Results

Cognigy processes simulations asynchronously. You submit the payload to /api/v1/simulations, receive a job ID, and poll /api/v1/simulations/{id} until the status resolves to COMPLETED or FAILED. You must implement retry logic for 429 Too Many Requests.

import time
import json

async def run_simulation(
    base_url: str,
    token: str,
    payload: SimulationPayload,
    poll_interval: float = 2.0,
    max_retries: int = 5
) -> Dict[str, Any]:
    headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
    body = payload.model_dump_json()
    
    async with httpx.AsyncClient() as client:
        # Submit simulation
        submit_resp = await client.post(
            f"{base_url}/api/v1/simulations",
            headers=headers,
            content=body
        )
        submit_resp.raise_for_status()
        job_id = submit_resp.json()["id"]
        
        # Poll with 429 retry logic
        retries = 0
        while True:
            try:
                poll_resp = await client.get(
                    f"{base_url}/api/v1/simulations/{job_id}",
                    headers=headers
                )
                if poll_resp.status_code == 429:
                    retries += 1
                    if retries > max_retries:
                        raise RuntimeError("Rate limit exceeded after maximum retries")
                    await asyncio.sleep(2 ** retries)
                    continue
                poll_resp.raise_for_status()
                result = poll_resp.json()
                
                if result.get("status") in ("COMPLETED", "FAILED"):
                    return result
                await asyncio.sleep(poll_interval)
            except httpx.HTTPStatusError as e:
                if e.response.status_code != 429:
                    raise

Full HTTP Cycle Example for Step 3

POST /api/v1/simulations HTTP/1.1
Host: myenv.cognigy.ai
Authorization: Bearer eyJhbGciOiJSUzI1NiIs...
Content-Type: application/json

{"botId":"bot-123","environmentId":"env-prod","versionId":"ver-abc","inputs":[{"text":"book flight to paris"}],"context":{},"assertions":[{"step_index":0,"expected_intent":"book_flight","expected_confidence_min":0.85,"expected_entities":{}}]}

HTTP/1.1 202 Accepted
Content-Type: application/json
{"id":"sim-xyz", "status":"RUNNING", "createdAt":"2024-01-15T10:30:00Z"}

GET /api/v1/simulations/sim-xyz HTTP/1.1
Host: myenv.cognigy.ai
Authorization: Bearer eyJhbGciOiJSUzI1NiIs...

HTTP/1.1 200 OK
Content-Type: application/json
{
  "id":"sim-xyz",
  "status":"COMPLETED",
  "durationMs":1240,
  "steps":[
    {
      "index":0,
      "userInput":"book flight to paris",
      "botResponse":"I can help with that. Which date?",
      "nlu": {"intent":"book_flight","confidence":0.92,"entities":[]}
    }
  ]
}

Step 4: Analyze NLU Metrics and Generate Audit Logs

After retrieval, you calculate intent matching accuracy, entity extraction precision, assertion success rates, and simulation duration. You also generate a structured audit log for governance compliance.

from datetime import datetime, timezone

class SimulationMetrics:
    def __init__(self, raw_result: Dict[str, Any], assertions: List[Assertion]):
        self.raw_result = raw_result
        self.assertions = assertions
        self.duration_ms = raw_result.get("durationMs", 0)
        self.steps = raw_result.get("steps", [])
        
    def calculate_metrics(self) -> Dict[str, Any]:
        total_assertions = len(self.assertions)
        passed_assertions = 0
        intent_scores = []
        entity_accuracy = []
        
        for assertion in self.assertions:
            step = next((s for s in self.steps if s.get("index") == assertion.step_index), None)
            if not step:
                continue
                
            nlu = step.get("nlu", {})
            actual_intent = nlu.get("intent", "")
            actual_confidence = nlu.get("confidence", 0.0)
            intent_scores.append(actual_confidence)
            
            # Check intent match and confidence threshold
            intent_match = actual_intent == assertion.expected_intent and actual_confidence >= assertion.expected_confidence_min
            
            # Check entity extraction accuracy
            expected_entities = assertion.expected_entities
            actual_entities = nlu.get("entities", [])
            extracted_keys = {e.get("type") for e in actual_entities}
            expected_keys = set(expected_entities.keys())
            entity_match = expected_keys.issubset(extracted_keys) if expected_keys else True
            entity_accuracy.append(1.0 if entity_match else 0.0)
            
            if intent_match and entity_match:
                passed_assertions += 1
                
        avg_intent_score = sum(intent_scores) / len(intent_scores) if intent_scores else 0.0
        avg_entity_accuracy = sum(entity_accuracy) / len(entity_accuracy) if entity_accuracy else 1.0
        assertion_success_rate = (passed_assertions / total_assertions * 100) if total_assertions > 0 else 0.0
        
        return {
            "duration_ms": self.duration_ms,
            "avg_intent_confidence": round(avg_intent_score, 4),
            "avg_entity_accuracy": round(avg_entity_accuracy, 4),
            "assertion_success_rate": round(assertion_success_rate, 2),
            "passed_assertions": passed_assertions,
            "total_assertions": total_assertions
        }
        
    def generate_audit_log(self) -> str:
        timestamp = datetime.now(timezone.utc).isoformat()
        log_entry = {
            "timestamp": timestamp,
            "simulation_id": self.raw_result.get("id"),
            "environment_id": self.raw_result.get("environmentId"),
            "bot_version": self.raw_result.get("versionId"),
            "metrics": self.calculate_metrics(),
            "steps": self.steps,
            "compliance_status": "PASS" if self.calculate_metrics()["assertion_success_rate"] == 100.0 else "FAIL"
        }
        return json.dumps(log_entry, indent=2)

Step 5: Synchronize Outcomes to MLOps Dashboard via Webhook

You push the calculated metrics and audit log to an external MLOps endpoint. This enables continuous improvement tracking and automated regression gating.

async def notify_mlops_dashboard(webhook_url: str, metrics: Dict[str, Any], audit_log: str) -> bool:
    payload = {
        "event": "cognigy_simulation_complete",
        "metrics": metrics,
        "audit_log": audit_log,
        "timestamp": datetime.now(timezone.utc).isoformat()
    }
    
    async with httpx.AsyncClient(timeout=10.0) as client:
        try:
            resp = await client.post(
                webhook_url,
                json=payload,
                headers={"Content-Type": "application/json", "X-Source": "cognigy-sim-runner"}
            )
            resp.raise_for_status()
            return True
        except httpx.HTTPError as e:
            print(f"Webhook notification failed: {e}")
            return False

Complete Working Example

The following script combines all components into a regression test runner. Replace the placeholder credentials and identifiers with your environment values.

import asyncio
import sys
import httpx
from typing import Dict, Any

# Import classes from previous sections
# CognigyAuth, validate_environment, SimulationPayload, Assertion, UserInput
# build_simulation_payload, run_simulation, SimulationMetrics, notify_mlops_dashboard

async def run_regression_test():
    # Configuration
    COGNIGY_BASE_URL = "https://myenv.cognigy.ai"
    AUTH_TOKEN_URL = "https://myenv.cognigy.ai/api/v1/auth/token"
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    WEBHOOK_URL = "https://mlops.internal.com/webhooks/cognigy-sim"
    
    BOT_ID = "bot-12345"
    ENV_ID = "env-staging"
    VERSION_ID = "ver-abc"
    
    # Initialize auth
    auth = CognigyAuth(CLIENT_ID, CLIENT_SECRET, AUTH_TOKEN_URL)
    token = await auth.get_token()
    
    # Step 1: Validate environment
    try:
        await validate_environment(COGNIGY_BASE_URL, token, BOT_ID, ENV_ID, VERSION_ID)
        print("Environment and bot version validated successfully.")
    except Exception as e:
        print(f"Validation failed: {e}")
        sys.exit(1)
        
    # Step 2: Build payload
    conversation_flow = ["book flight to paris", "departing next monday", "confirm booking"]
    expected_responses = [
        {"intent": "book_flight", "min_confidence": 0.85, "entities": {}},
        {"intent": "provide_date", "min_confidence": 0.80, "entities": {"date": "next monday"}},
        {"intent": "confirm_booking", "min_confidence": 0.90, "entities": {}}
    ]
    initial_context = {"user_id": "test-user-001", "locale": "en-US"}
    
    payload = build_simulation_payload(BOT_ID, ENV_ID, VERSION_ID, conversation_flow, expected_responses, initial_context)
    
    # Step 3: Execute simulation
    print("Submitting simulation...")
    raw_result = await run_simulation(COGNIGY_BASE_URL, token, payload)
    print(f"Simulation completed with status: {raw_result.get('status')}")
    
    # Step 4: Analyze metrics
    metrics_analyzer = SimulationMetrics(raw_result, payload.assertions)
    metrics = metrics_analyzer.calculate_metrics()
    audit_log = metrics_analyzer.generate_audit_log()
    
    print("Metrics:")
    print(json.dumps(metrics, indent=2))
    print("Audit Log:")
    print(audit_log)
    
    # Step 5: Notify MLOps
    await notify_mlops_dashboard(WEBHOOK_URL, metrics, audit_log)
    print("MLOps dashboard notified.")

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

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired bearer token or invalid client credentials.
• Fix: Ensure the CognigyAuth class refreshes the token when expires_at is reached. Verify that the OAuth client has the simulation:execute scope assigned in the Cognigy admin console.
• Code Fix: The get_token method already checks datetime.now(timezone.utc) < self.expires_at. If you receive 401 during polling, force a refresh by calling await auth.get_token() before the poll loop.

Error: 403 Forbidden

• Cause: Environment isolation rules block the bot ID, or the OAuth scope lacks environment:read.
• Fix: Check the allowed_bot_ids array in the environment configuration. Add the bot ID to the whitelist or switch to a permissive environment for testing.
• Code Fix: The validate_environment function explicitly raises PermissionError when strict isolation blocks the bot. Adjust the environment configuration in Cognigy.AI before retrying.

Error: 400 Bad Request

• Cause: Simulation payload schema mismatch or missing required fields.
• Fix: Verify that botId, environmentId, versionId, and inputs are present. Ensure assertions use zero-based step_index values that align with the inputs array length.
• Code Fix: Use SimulationPayload.model_dump_json() to validate against Pydantic constraints before sending. Print payload.model_dump() to inspect the exact JSON structure.

Error: 429 Too Many Requests

• Cause: Exceeding Cognigy.AI rate limits during simulation polling or submission.
• Fix: Implement exponential backoff. The run_simulation function includes a max_retries loop that sleeps for 2 ** retries seconds. Increase poll_interval to reduce request frequency.
• Code Fix: Adjust poll_interval=5.0 in the function call if your environment enforces stricter limits.

Error: 500 Internal Server Error

• Cause: Bot version contains broken logic, missing intents, or environment runtime failure.
• Fix: Review the bot version in the Cognigy.AI console. Check the simulation step logs for NLU parsing failures. Roll back to a stable version if the error persists.
• Code Fix: Catch httpx.HTTPStatusError in the poll loop and inspect poll_resp.text for Cognigy-specific error codes.

Official References

• Cognigy.AI API Documentation
• Cognigy.AI Simulation Endpoint Reference
• Cognigy.AI OAuth2 Authentication Guide
• httpx Async Client Documentation
• Pydantic V2 Schema Validation