Implementing Automated Load Testing for IVR and Bot Flows using Synthetic Interaction Generators
What This Guide Covers
You are building an automated load testing framework that injects synthetic voice and digital interactions into your Genesys Cloud IVR and bot flows at production-level volumes - validating that the system behaves correctly under load, identifying flow bottlenecks and Data Action timeout thresholds before they surface in production, and generating actionable capacity reports for your quarterly business review. When complete, a pre-release load test simulates 500 concurrent calls through your Architect IVR in 10 minutes, surfacing any queue overflow, Data Action latency, or bot API rate limit issues with full call-level traces.
Prerequisites, Roles & Licensing
- Genesys Cloud: A dedicated non-production (staging/UAT) org for load testing - never run load tests against production
- Load generation infrastructure: Asterisk or FreeSWITCH for SIP call generation; Locust or k6 for digital channel (web messaging/API) load
- Permissions in the staging org:
Telephony > DID > Edit(to configure test DIDs for call injection)Architect > Flow > Edit(to instrument flows with test hooks)Analytics > Conversation Detail > View(to extract post-test results)
- BYOC trunk: A BYOC Cloud or BYOC Premises SIP trunk configured in the staging org that accepts calls from your load testing infrastructure
The Implementation Deep-Dive
1. Load Testing Architecture for Genesys Cloud
Genesys Cloud does not expose a “synthetic call” API - you cannot inject calls programmatically through the REST API. All load testing must use the actual telephony or digital channel entry points:
Voice load testing:
[Asterisk/FreeSWITCH Load Generator]
→ [SIP INVITE to Genesys Cloud BYOC Edge/Cloud]
→ [Inbound call handler in Architect]
→ [Bot/IVR flow under test]
Digital (web messaging) load testing:
[k6/Locust Web Messaging Client]
→ [POST to Genesys Cloud Messenger API]
→ [Inbound message flow in Architect]
→ [Bot flow under test]
The Trap - load testing production infrastructure directly: Even in a “test” campaign, injecting 500 simultaneous calls into production Genesys Cloud can consume real telephony capacity, generate real Genesys Cloud usage charges, and trigger real routing to real agents. Always use a dedicated staging Genesys Cloud org with test trunks, test queues, and no live agents.
2. SIP-Based Voice Load Generation with Asterisk
Asterisk load generation script (generates N simultaneous calls):
#!/bin/bash
# generate_load.sh - Inject concurrent SIP calls to Genesys Cloud staging BYOC trunk
CONCURRENT_CALLS=${1:-50}
CALL_DURATION=${2:-120} # seconds
TARGET_DID="${3:-+15005551234}" # Genesys Cloud staging test DID
ASTERISK_HOST="127.0.0.1"
ASTERISK_PORT="5038"
AMI_USER="loadtest"
AMI_PASS="loadtest123"
echo "Injecting $CONCURRENT_CALLS concurrent calls to $TARGET_DID for ${CALL_DURATION}s"
for i in $(seq 1 $CONCURRENT_CALLS); do
# Originate call via Asterisk Manager Interface (AMI)
{
echo "Action: Originate"
echo "Channel: SIP/genesys_trunk/${TARGET_DID}"
echo "Exten: s"
echo "Context: loadtest"
echo "Priority: 1"
echo "CallerID: +1800${i}555${RANDOM}" # Unique ANI per call
echo "Timeout: 30000"
echo "Variable: LOAD_TEST_ID=${i},LOAD_TEST_BATCH=$(date +%s)"
echo ""
} | nc -q 1 $ASTERISK_HOST $ASTERISK_PORT
sleep 0.05 # 50ms stagger - don't inject all calls simultaneously (avoids SIP flood)
done
echo "Load injection complete. Calls will auto-hangup after ${CALL_DURATION}s."
Asterisk dial plan for synthetic callers (extensions.conf):
[loadtest]
; Synthetic caller - simulates DTMF IVR navigation
exten => s,1,Answer()
exten => s,2,Wait(3) ; Allow IVR to answer
exten => s,3,SendDTMF(1) ; Press 1 for "Sales"
exten => s,4,Wait(5) ; Wait for next menu
exten => s,5,SendDTMF(2) ; Press 2 for "Product Info"
exten => s,6,Wait(${CALL_DURATION}) ; Stay in flow for configured duration
exten => s,7,Hangup()
For bot flow testing, synthesize speech input using Amazon Polly audio files played into the call via Asterisk’s Playback application - the bot hears realistic speech, not DTMF:
[loadtest_bot]
exten => s,1,Answer()
exten => s,2,Wait(2)
exten => s,3,Playback(/var/lib/asterisk/sounds/test/order_status_query) ; "What is my order status?"
exten => s,4,Wait(8) ; Allow bot to respond
exten => s,5,Playback(/var/lib/asterisk/sounds/test/account_number) ; "My account number is 1234567"
exten => s,6,Wait(10)
exten => s,7,Hangup()
Generate the Polly audio files ahead of time:
import boto3
import os
polly = boto3.client("polly", region_name="us-east-1")
test_phrases = {
"order_status_query": "What is my order status?",
"account_number": "My account number is one two three four five six seven",
"yes": "Yes",
"no": "No",
"cancel": "Cancel my subscription",
"agent_request": "I need to speak to an agent"
}
for filename, text in test_phrases.items():
resp = polly.synthesize_speech(
Text=text,
OutputFormat="pcm",
VoiceId="Joanna",
SampleRate="8000" # 8kHz for telephone audio
)
# Save as raw PCM, then convert to WAV/GSM for Asterisk
with open(f"/tmp/{filename}.pcm", "wb") as f:
f.write(resp["AudioStream"].read())
os.system(f"sox -r 8000 -e signed -b 16 -c 1 /tmp/{filename}.pcm /var/lib/asterisk/sounds/test/{filename}.gsm")
print("Audio files generated.")
3. Digital Channel Load Testing with k6
For web messaging and bot flow load testing without telephony infrastructure:
// k6 load test: web messaging bot flow
import http from 'k6/http';
import { sleep, check } from 'k6';
import { SharedArray } from 'k6/data';
const GENESYS_DEPLOYMENT_ID = "your-staging-deployment-id";
const GENESYS_ORG_ID = "your-staging-org-id";
const BASE_URL = "https://api.mypurecloud.com";
export const options = {
scenarios: {
ramp_up: {
executor: 'ramping-vus',
startVUs: 0,
stages: [
{ duration: '2m', target: 50 }, // Ramp to 50 concurrent sessions
{ duration: '5m', target: 200 }, // Ramp to 200 concurrent sessions
{ duration: '3m', target: 200 }, // Sustain 200 sessions
{ duration: '2m', target: 0 }, // Ramp down
]
}
},
thresholds: {
'http_req_duration': ['p(95)<3000'], // 95% of requests under 3 seconds
'http_req_failed': ['rate<0.01'], // <1% error rate
}
};
export default function() {
// Step 1: Create a new messaging session
const sessionResp = http.post(
`${BASE_URL}/api/v2/webmessaging/sessions`,
JSON.stringify({
deploymentId: GENESYS_DEPLOYMENT_ID
}),
{ headers: { 'Content-Type': 'application/json' } }
);
check(sessionResp, {
'session created': (r) => r.status === 200 || r.status === 201,
});
const session = sessionResp.json();
const sessionId = session.id;
const token = session.token;
sleep(1); // Simulate customer reading the greeting
// Step 2: Send initial message
const msgResp = http.post(
`${BASE_URL}/api/v2/webmessaging/messages`,
JSON.stringify({
deploymentId: GENESYS_DEPLOYMENT_ID,
sessionId: sessionId,
token: token,
message: {
type: 'Text',
text: 'What are your business hours?'
}
}),
{ headers: { 'Content-Type': 'application/json' } }
);
check(msgResp, {
'message sent': (r) => r.status === 200,
});
sleep(3); // Wait for bot response
// Step 3: Send follow-up
http.post(
`${BASE_URL}/api/v2/webmessaging/messages`,
JSON.stringify({
deploymentId: GENESYS_DEPLOYMENT_ID,
sessionId: sessionId,
token: token,
message: {
type: 'Text',
text: 'Thank you, goodbye'
}
}),
{ headers: { 'Content-Type': 'application/json' } }
);
sleep(1);
}
Run with:
k6 run --out json=results.json genesys_load_test.js
4. Instrumenting Architect Flows for Load Test Observability
Standard Genesys Cloud analytics don’t easily distinguish load test traffic from production traffic. Add a test identifier to each synthetic call:
In Architect - detect and tag load test calls:
[Inbound Call]
→ [Set Variable: isLoadTest = ANI.startsWith("+18005")] // Test ANI prefix
→ [Decision: isLoadTest == true]
YES → [Set Participant Data: loadTestBatch = LOAD_TEST_BATCH, loadTestId = LOAD_TEST_ID]
NO → (normal routing continues)
After the load test, query Analytics filtering on the loadTestBatch attribute to extract only synthetic call records:
def get_load_test_results(batch_id: str, access_token: str, base_url: str) -> dict:
resp = requests.post(
f"{base_url}/api/v2/analytics/conversations/details/query",
headers={"Authorization": f"Bearer {access_token}", "Content-Type": "application/json"},
json={
"segmentFilters": [
{
"type": "and",
"predicates": [
{
"type": "dimension",
"dimension": "participantData",
"operator": "matches",
"value": f"loadTestBatch={batch_id}"
}
]
}
],
"metrics": ["nConnected", "tHandle", "tAbandon", "tIvr", "nTransferred"]
}
)
return resp.json()
5. Generating the Load Test Report
def generate_load_test_report(results: dict, target_concurrent: int) -> dict:
conversations = results.get("conversations", [])
durations = [
c.get("conversationEnd", 0) - c.get("conversationStart", 0)
for c in conversations
if c.get("conversationEnd")
]
abandoned = sum(1 for c in conversations if c.get("sessions", [{}])[0].get("disconnectType") == "SELF")
return {
"summary": {
"targetConcurrent": target_concurrent,
"totalCallsInjected": len(conversations),
"callsCompletedSuccessfully": len(conversations) - abandoned,
"abandonRate": f"{abandoned / max(len(conversations), 1) * 100:.1f}%",
"avgCallDurationMs": sum(durations) / max(len(durations), 1),
"p95DurationMs": sorted(durations)[int(len(durations) * 0.95)] if durations else 0,
"maxConcurrentObserved": "query_realtime_queue_stats()",
},
"bottlenecks": identify_bottlenecks(conversations),
"recommendation": generate_capacity_recommendation(conversations, target_concurrent)
}
Validation, Edge Cases & Troubleshooting
Edge Case 1: Staging Org Rate Limits Differ from Production
Genesys Cloud applies per-org API rate limits that may be lower in sandbox/trial orgs than production. If your load test hits API rate limits in staging but not in production, the results are not predictive of production behavior. Verify with your Genesys Cloud account team that your staging org has equivalent rate limit entitlements to your production org before relying on staging load test results.
Edge Case 2: Bot NLU Saturation Under Load
If your bot uses a third-party NLU (Dialogflow CX, Amazon Lex), the NLU platform has its own rate limits independent of Genesys Cloud. 200 concurrent bot sessions each sending a message every 5 seconds = 40 NLU requests/second. Verify your Dialogflow CX or Lex quotas before the load test; request quota increases if needed. Failure to do so causes bot No Input fallbacks under load even when the Genesys Cloud infrastructure is healthy.
Edge Case 3: DTMF vs. Speech Recognition Under Load
ASR (Automatic Speech Recognition) for IVR prompts has higher latency than DTMF processing - speech barge-in requires transcription before branching. Under load, ASR latency increases as the speech processing capacity is shared across all concurrent calls. Run separate load tests for DTMF-only IVR paths and speech recognition paths - don’t mix them in the same load test, as their performance profiles differ significantly.
Edge Case 4: Cleaning Up After a Load Test
500 synthetic calls each generating a conversation record, recording, and analytics event in your staging org can accumulate quickly across multiple test runs. Implement a post-test cleanup script that deletes conversation recordings (to manage storage costs) and archives conversation metadata. Check Genesys Cloud’s data retention settings for your staging org to prevent indefinite accumulation.