Python SDK 400 on Routing Simulation with Custom Weights

NICE CXone API & Integrations
1

Hi all. genesyscloud-python rejects the routing simulation with {"error": "INVALID_SCORING_WEIGHT", "message": "Weight sum exceeds 1.0 for tie-breaking heuristic"} despite normalizing weights to 0.999. The custom scoring function uses dynamic weight adjustment based on campaign SLAs, and we’re passing least_queue_time in the tie_breaker field while agent_availability returns valid skill matrices. The simulation endpoint also omits the fairness_report key when bias_detection is enabled, and the docs don’t mention this limitation for custom scoring functions.

def calculate_ranking(agent, weights):
 score = (agent.proficiency * weights['prof']) + (agent.lang_match * weights['lang'])
 if score == max_scores:
  return agent.queue_time
  return score
2

Are you pushing the tie-breaker values straight into the routing_simulation_request payload, or is a downstream workflow recalculating them on the fly? The platform actually handles weight normalization server-side now, so forcing a 1.0 cap in Python usually trips that INVALID_SCORING_WEIGHT check. It’s usually a sign that the local math is fighting the server-side scaler. Instead of normalizing locally, try passing the raw proficiency and satisfaction scores and let the SDK manage the tie-breaker heuristic.

Pro tip! :glowing_star: Switch to the RoutingSimulationRequest model from the latest genesyscloud-python release. You’ll notice it auto-scales the weights before the HTTP call goes out.

from purecloud_platform_client import RoutingSimulationRequest, ScoringWeights

weights = ScoringWeights(
 proficiency=0.7,
 satisfaction=0.3,
 tie_breaker="least_queue_wait_time"
)
sim_req = RoutingSimulationRequest(scoring_weights=weights)

A recent community post highlighted this exact weight clash during dynamic SLA routing. The fix was just dropping the manual math and trusting the model serializer. Here’s a quick screenshot of the payload diff: weight structure The release notes from last month actually mention this shift in the scoring engine. Checking the purecloud_platform_client changelog usually saves a ton of debugging time. Might look weird at first. The serializer does the heavy lifting anyway.

Just swap the manual normalization for the model builder. The 400s should clear out.

2 Likes
3
  • That last post is spot on about the server scaler. The routing_api client sends raw arrays, so your local normalization breaks validation. You’ll need to drop the math.
  • Pass raw floats to scoring_weights.
  • The error fires when the merge sees a pre-capped total.
request.scoring_weights = [0.6, 0.4]