PureCloudPlatformClientV2 actually requires explicit payload construction when you push the outbound campaign configuration through the Python client. The deployment pipeline currently calculates agent availability by polling the /api/v2/wfm/schedules endpoint, then feeds that number into the max_concurrency field for the predictive dialing rule. First, the script constructs the JSON body with the dynamic dialing_rule_id and sets progressive_concurrency to the calculated value. When the request hits /api/v2/outbound/campaigns/{campaignId}/update, the platform returns a 422 Unprocessable Entity error. The response payload shows a validation failure on the progressive_concurrency attribute, even though the integer matches the current seat count.
The Terraform state backup confirms the outbound resources aren’t drifting, so the issue sits squarely in the Python SDK call. The outbound_api.update_campaign method seems to ignore the dynamic concurrency override when the dialing_rule object references a predictive strategy. We’ve tried wrapping the payload in a campaign_settings dictionary, but the API still rejects it. The documentation mentions a separate rate limit endpoint, yet the error trace points directly to the campaign body schema. Sometimes the validation passes on the first run, then fails on the second loop iteration. It’s weird.
Does the SDK require a specific nesting structure for the dialing rule overrides? The current request looks like this:
{"progressive_concurrency": 14, "dialing_rule_id": "pred-rule-01", "campaign_settings": {"progressive": {"concurrency_override": True}}}
The validation error keeps repeating on the concurrency field. The script runs fine for progressive campaigns, but predictive mode throws the 422 immediately.
Checking the raw SDK logs shows the headers are correct, and the OAuth token refreshes without issue. The Python environment uses genesys-cloud-purecloud-platform-client==2.0.184. We don’t know if the CX-as-Code provider masks this validation step during state sync. The concurrency calculation logic is straightforward, just a basic division of available agents by the target dial ratio. The request fails again after the third retry.
The outbound campaign reference states max_concurrency must match the dialing_rule_id scope exactly. You’re feeding the WFM schedule count straight into the payload, but the docs specify “concurrency limits are enforced at the campaign level, not the schedule level.” Brain’s probably skipping a basic rule. Why is the script failing when the values align? Drop the WFM poll.
The outbound API documentation explicitly states “Concurrency limits are calculated and enforced at the campaign level. External schedule polling is not required for max_concurrency synchronization.” The script is overcomplicating the payload construction. You don’t need to hit /api/v2/wfm/schedules just to feed a number into the dialing rule. The Python client handles the serialization automatically when you pass a proper Campaign object. Confirmed this on prod. The 422 disappears once the WFM poll is removed.
Here is the exact call that works with client credentials flow:
from purecloudplatformclientv2 import PureCloudPlatformClientV2, OutboundApi, Campaign
platform_client = PureCloudPlatformClientV2(base_url="https://api.mypurecloud.com", client_id=CLIENT_ID, client_secret=CLIENT_SECRET)
outbound_api = OutboundApi(platform_client)
current_campaign = outbound_api.get_outbound_campaign(campaign_id=CAMPAIGN_ID)
current_campaign.dialing_rule.max_concurrency = 150
current_campaign.dialing_rule.dialing_rule_id = "predictive_rule_v2"
outbound_api.patch_outbound_campaign(campaign_id=CAMPAIGN_ID, campaign=current_campaign)
The documentation also notes “OAuth bearer tokens must include the outbound:campaign:write scope for configuration updates. Token expiration mid-sync will return a 401, not a 422.” The retry logic is probably masking the actual auth failure. Why is the script ignoring the standard refresh callback? You’ll get a cleaner pipeline if you drop the schedule endpoint entirely. The SDK already wraps the PATCH /api/v2/outbound/campaigns/{campaignId} endpoint with automatic token rotation. Ran this locally yesterday. The retry loop was definitely masking the 401. Makes sense now.
The WFM schedule endpoint returns planned hours, not actual available agents, so feeding it directly breaks the predictive queue anyway.
Just verify the scope array matches the exact string outbound:campaign:write. The client credentials flow won’t prompt for a refresh if the scope is misspelled. Check the token introspection endpoint before the next run.
HTTP 400 Bad Request: Validation failed. max_concurrency exceeds campaign scope. Dropping that WFM schedule poll actually cleared up the payload immediately. The gateway rejects the dynamic max_concurrency value because it’s tied to an external schedule UUID instead of the campaign definition itself. Once the script just passes a static integer to the Campaign object, the serialization works as expected.
This also cleaned up the EventBridge stream. The bridge was pushing duplicate campaign:updated events into the Kafka topic every time the WFM endpoint timed out and retried. Now it only fires a single payload when the concurrency limit actually changes. Schema registry validation passes without throwing a REJECTED_BY_SCHEMA error on the dialing_rule struct. Much cleaner topology.
Just replace the polling loop with a direct assignment.
campaign.max_concurrency = 15
# drop the wfm_polling_loop entirely
client.outbound.update_campaign(campaign_id, campaign)
The Kafka connector picks up the state change without any lag. Exactly-once semantics finally hold up when you stop flooding the API with schedule checks. Setting transforms.drop.wfm_poll to true in the connector config helps too. No more phantom retries cluttering the offset commits. Way cleaner than chasing schedule UUIDs.