Looking for advice on correlating voice bot session IDs with Genesys Cloud recording UUIDs for legal hold exports. Our UK GDPR audit requires a strict chain of custody for AI interactions.
The GET /api/v2/recordings/{recordingId} response lacks the botSessionId field. Is there a reliable method to map these records during bulk export to S3, or must we rely on timestamp matching?
What’s happening here is that the recording API is designed for media storage, not conversational context. It intentionally decouples the audio blob from the session metadata to maintain separation of concerns within the platform architecture. Relying on timestamp matching is fragile and will fail under high concurrency or during network jitter events.
For a robust solution that satisfies strict audit requirements, you need to leverage the Interaction API to bridge the gap. The interaction entity holds the externalContactId or custom attributes that map directly to your bot session identifiers. Here is the recommended workflow:
botSessionId into a custom attribute (e.g., custom:botSessionId) on the interaction object via the Interactions API or Event Streams during the session.GET /api/v2/analytics/interactions/summary or the detailed interaction endpoint to fetch all interactions within your target export window. Filter by mediaTypes: ["voice"] and wrapupCodes if applicable to narrow the scope.recordings array. Extract the recordingId from this array. This creates a deterministic map: interactionId -> recordingId and interactionId -> custom:botSessionId.recordingId list to fetch the actual media files or metadata from the Recording API.This approach ensures a 1:1 mapping without relying on temporal heuristics. It also aligns with AppFoundry best practices for data integrity in multi-org environments. If you are building a Premium App, consider caching this mapping in your external database during the interaction lifecycle to reduce API load during bulk exports. This pattern also handles edge cases where multiple recordings exist for a single interaction (e.g., split leg calls).
You need to abandon the idea of mapping recordings directly via the Interaction API if your goal is high-throughput load validation. The Interaction endpoint has strict rate limits that will choke your JMeter threads almost immediately, especially when you scale past 50 concurrent requests. Instead, look at the analytics/conversations/details/query endpoint. It returns the id for the interaction which links directly to the recording metadata in the backend, but more importantly, it includes the botSessionId if the conversation involved a voice bot. This approach is significantly faster for bulk data retrieval during performance testing. The recording API is designed for media retrieval, not metadata aggregation, so hitting it repeatedly for session IDs is a recipe for 429 errors.