Python SDK 400 on Routing Simulation with Custom Weights

{"error": "INVALID_SCORING_WEIGHT", "message": "Weight sum exceeds 1.0 for tie-breaking heuristic"}

genesyscloud-python rejects the routing simulation request despite normalizing the proficiency and satisfaction weights. The custom scoring function calculates agent rankings using a dynamic weight adjustment based on campaign SLAs, but the API complains about the tie-breaking scenario. We’re passing the least_queue_time heuristic in the tie_breaker field, and the sum is definitely 0.999. The agent_availability query returns valid skill matrices, so the input data looks clean.

The fairness report generation fails silently after the simulation completes. Here’s the snippet handling the weight calculation:

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

The simulation endpoint /api/v2/routing/simulation is supposed to expose results for QA testing. The bias_detection flag is enabled, but the response payload omits the fairness metrics. genesyscloud-python documentation doesn’t mention this limitation for custom scoring functions. The fairness_report key is absent. Response structure looks truncated.

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! 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: 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.

• 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]