Hey all, just wondering if anyone’s wrestled with updating predictive routing model weights through the API lately? We’re running an A/B test - variant A is the existing model, variant B has a slightly tweaked agent scoring function, aiming for better handle time. Variant B is showing a noticeable improvement - roughly 8% better handle time, which is pretty sweet. But getting the weights updated reliably is…painful.

The API call itself - the request to update the predictive routing model weights - keeps failing intermittently with a 400 Bad Request. It’s not consistent, sometimes it works first try, sometimes it takes ten attempts. And the error message is just “Invalid request body”. The body looks fine to me though. I’m sending a simple JSON payload with the new weights for each skill.

It’s like the API parser is being picky about the formatting. Studio’s REST proxy action is doing jack all - it’s a straight pass-through, so no extra formatting shenanigans there. I’ve checked the Content-Type header - it’s set to application/json, naturally. We’re using the Node.js SDK, version 2.11.1. It’s almost like it’s a timing issue, but I can’t nail that down.

Here’s a sample payload that’s causing the issue. It’s a pretty typical structure.

{
 "routing_skills": {
 "skill_123": 0.6,
 "skill_456": 0.4,
 "skill_789": 0.2
 }
}

We’ve been digging around, and here’s what we’ve tried:

• SDK version: Node.js SDK 2.11.1.
• API Version: The predictive routing model update endpoint.
• Content-Type: Verified as application/json.
• Payload format: Confirmed consistent JSON structure.
• Retry logic: Added exponential backoff with jitter to the API calls. Helps, but doesn’t eliminate the issue.
• Studio: Tested calling the same API call directly through Studio, and the problem persists.
• Model ID: Double-checked the modelId is correct.
• A/B test setup: Verified the test split is functioning correctly.

It’s really starting to mess with the test results because we’re constantly having to re-attempt the weight updates. Anyone else hitting this? Is there a known quirk to the API that we’re missing? Maybe it’s a regional thing? We’re in Berlin, so our instance is in Europe. The mic stays hot.

pseudo-code - predictive routing model weight update

init - get current model weights

- retrieve current model weights

- extract weights array

iterate through weights array

- for each weight, calculate delta

- validate delta against max_delta_config

- if delta is valid, construct payload

- payload includes modelId, weight_id, new_weight

- update the model weight

- handle API errors - rate limits, validation failures

- if API fails, log error, implement retry logic

- if all weights updated, log success

ok so, that API is kinda finicky - seen it before. small thing, but you’re probably hitting the weight validation limits? from what i’ve seen, the API isn’t super clear about what the limits are, but it’s def there.

the code above sketches out a process - it’s more of an algorithm than actual code, but the idea is to get the current weights first, then iterate through them, calculate the change, and only apply the change if it’s within the allowed range. a lot of issues can be avoided if you validate the deltas before sending the PATCH request.

i also recommend building in some retry logic, the API can be rate limited if you’re updating a ton of weights at once - it’s been repro’d on our side a bunch.

That’s right about the filters-we’ve seen similar issues with the API v2 endpoints. Sometimes the parser gets confused.

Cause:
The Edge server’s BIOS has a setting for TCP checksum offload-if that’s enabled, it can cause problems with the PATCH requests.

Solution:
Try disabling it in the BIOS settings-we had this problem last year, someone posted on the community about it. Also, check the MTU size-sometimes a mismatch can cause the payload to get truncated.

Pro tip! We’ve dealt with similar weight drift issues. It’s usually a caching quirk with the predictive model update. Try pushing the update via a PUT request to the specific model ID instead of a bulk update. Keep it simple!

{
 "name": "Predictive Model B",
 "weights": [0.8, 0.2]
}

Check the docs here: Genesys Cloud Developer Center