What’s the best way to handle 429 throttling errors when querying /api/v2/analytics/interactions/query from within a high-volume architect flow? we are processing inbound call metadata for our 15 byoc trunks in the singapore region. the flow triggers a rest api request immediately after call disposition to log custom analytics data. during peak hours, we see a spike in 429 too many requests responses from the gen cloud analytics api. the standard retry logic in the rest api block is causing further queue buildup and increased latency for agent wrap-up. i have reviewed the rate limit documentation, but it is unclear if there is a specific header or payload optimization for bulk interaction queries initiated from within architect. currently, we are staggering the query start times based on org id to avoid the burst limit, but with 15 trunks, the volume is still hitting the ceiling. does anyone have a robust pattern for handling this without dropping data or significantly impacting agent experience? we are using the latest rest api block version in architect. any insights on carrier-specific quirks or failover logic that might affect this would also be appreciated.
you need to stop querying the analytics api directly from architect flows. it is not designed for high concurrency and will throttle you. use the bulk export api with s3 integration instead for legal hold data.
The suggestion above aligns with enterprise best practices for data retention. The Analytics API is strictly rate-limited to protect platform stability, making it unsuitable for high-volume transactional logging within Architect flows. Direct REST calls from every call disposition will inevitably trigger 429 throttling during peak concurrency. A more robust architectural pattern involves leveraging the Genesys Cloud Data Stream or Bulk Export capabilities to capture interaction metadata asynchronously. This approach decouples the real-time call flow from the analytics ingestion process. The Performance dashboard metrics remain accurate because they rely on internal database aggregations rather than external API calls. Integrating with an S3 bucket via Bulk Export allows for comprehensive historical analysis without impacting the live agent experience or consuming valuable API quota. This method ensures that custom analytics data is preserved for legal hold purposes while maintaining system performance. The configuration requires setting up a Data Stream subscription for interaction events and defining the appropriate S3 destination.