Bulk Export Job Fails with 400 Bad Request when Filtering by Predictive Routing Skill Group for Legal Discovery

Genesys Cloud Routing
1

Hi all,

We are encountering a consistent failure when attempting to export recording metadata for legal discovery requests involving our Predictive Routing campaigns. Our environment is Genesys Cloud (London region). We need to isolate conversations handled by a specific skill group (‘Legal_Support_Tier1’) to ensure compliance with data retention policies and chain of custody requirements.

We are using the Bulk Export API (POST /api/v2/bulk/recordings/export) with the following filter criteria in the request body:

{
 "type": "recordings",
 "dateRange": {
 "startDate": "2023-10-01T00:00:00.000Z",
 "endDate": "2023-10-31T23:59:59.999Z"
 },
 "filters": [
 {
 "type": "skill",
 "op": "contains",
 "value": "Legal_Support_Tier1"
 }
 ]
}

The job initiates but immediately fails with a 400 Bad Request error. The error response body contains:

{
 "code": "bad_request",
 "message": "Filter type 'skill' is not supported for bulk recording exports in predictive routing contexts.",
 "details": "Use 'routingQueue' or 'agent' filters instead."
}

This is problematic because Predictive Routing does not use static queues in the same manner as standard routing; skills are the primary assignment mechanism. If we filter by routingQueue, we lose the granularity required for our legal hold, as multiple skill groups may share a queue.

Has anyone found a workaround to export recordings filtered specifically by skill group for Predictive Routing conversations? We need to maintain an accurate audit trail for these specific interactions. Any insights on alternative API endpoints or Architect flow modifications that could tag these conversations for easier export would be appreciated.

Thanks.

2

Filtering by skill group directly in the initial POST /api/v2/bulk/recordings/export request is a common trap, especially for compliance-heavy use cases like legal discovery. The Bulk Export API does not support direct skill group filtering in the initial job creation payload in the way you might expect from the standard recordings search API. This mismatch is likely causing the 400 Bad Request.

The robust pattern we use for our AppFoundry partners is a two-step approach:

  1. Pre-filter via Search API: First, use GET /api/v2/recordings/search with a Lucene query to identify the specific recording IDs associated with the Legal_Support_Tier1 skill group. You can filter by routing.skills and your desired date range here.
{
"query": "routing.skills.name:Legal_Support_Tier1 AND timestamp:[2023-10-01T00:00:00.000Z TO 2023-10-31T23:59:59.999Z]",
"size": 100
}

Paginate through these results to collect all relevant id values.

  1. Export by ID List: Then, pass this curated list of IDs into the Bulk Export job.
{
"recordingIds": ["id1", "id2", "id3"],
"format": "csv",
"includeTranscripts": true
}

This method ensures you only export exactly what is needed, reducing data volume and avoiding the schema validation errors triggered by unsupported filters in the export payload. It also provides a clear audit trail of which recordings were selected before export, which is crucial for chain of custody. Keep in mind that if the result set from the search API is massive (thousands of recordings), you may need to batch the IDs into multiple export jobs to stay within API rate limits and job size constraints.

3

The suggestion above is correct. The Bulk Export API payload structure is strict and often rejects complex nested filters directly in the initial POST.

For compliance workflows, especially in AU/London regions, I prefer handling this via Terraform data sources to fetch the valid query ID first, then triggering the export via CLI or API. This separates the query validation from the job submission.

Here is the HCL pattern:

data "genesyscloud_analytics_query" "legal_records" {
 query = "conversationId IN (SELECT id FROM conversation WHERE skillName = 'Legal_Support_Tier1')"
 type = "conversation"
}

resource "genesyscloud_bulk_export" "discovery_job" {
 job_type = "RECORDINGS"
 query_id = data.genesyscloud_analytics_query.legal_records.id
 file_format = "CSV"
}

This ensures the query is valid before the export job starts. Direct skill filtering in the bulk payload often triggers schema validation errors. Use the analytics query data source to generate the compliant query ID, then pass that ID to the bulk export resource.