Running an A/B test on the new predictive routing model shows variant B crushing variant A on handle time, but the API keeps killing the deployment. It’s throwing a 422 Unprocessable Entity with "error": "model_weight_sum_exceeds_unity" even though the JSON payload definitely adds up to 1.0 across both splits, and you’ll notice the ML inference latency jumping to 320ms right before the call drops. Berlin edge runs 2024.10.1 build with the v2.1.4 SDK, stripping the variant_metadata object won’t fix the batch update rejection, and the AI scoring queue just stalls doing jack all.
[screenshot of payload and 422 response]
{
"weights": { "variant_a": 0.5, "variant_b": 0.5 },
"deployment_target": "prod_eu1"
}
HTTP 422 Unprocessable Entity with {"error": "model_weight_sum_exceeds_unity"} hits you because the JSON serializer is dropping trailing zeros on your float values. The routing engine doesn’t parse 0.5 and 0.5 as exactly 1.0 during the internal checksum. It treats them as 0.5000000000000001 after the parse. You’ll need to force explicit decimal precision.
Patch the scheme with strict string weights instead:
curl -X PUT "https://api.mypurecloud.com/api/v2/routing/predictiveroutingschemes/{schemeId}" \
-H "Authorization: Bearer {token}" \
-H "Content-Type: application/json" \
-d '{"variantWeights": [{"variantId": "v1", "weight": "0.50"}, {"variantId": "v2", "weight": "0.50"}]}'
If you’re wiring this through PureCloudPlatformClientV2, just run the weight array through a .toFixed(2) map before constructing the UpdatePredictiveRoutingSchemeRequest. The routing:predictiveroutingscheme:write scope handles the validation pass. Math breaks fast in JSON parsers anyway. You’ll save yourself three more 422 cycles if you normalize the array sum client-side. The latency spike you mentioned is just the model re-queueing requests while it waits for the weight reconciliation to finish. Check the calibration window lock status before you hit retry.