agent-transcript-logging

To log AI agent conversations for audit, implement a write-before-render contract that captures every learner message and agent response using a comprehensive schema before displaying any output. This guarantees an auditable trail and prevents data loss.

A write-before-render contract for AI conversation history is a mechanism that guarantees transcript entries are saved to storage before any agent response is displayed to the user. This ensures that no conversation data is lost if rendering fails.

Yes, you can isolate demo data from tenant-specific conversation transcripts by utilizing dedicated namespaces. This data retention and isolation approach manages the transcript lifecycle, including a 2-year retention policy, while keeping demo records separate.

You link escalation events to specific AI transcript entries by connecting critical flags, such as a DISTRESS signal, directly to the corresponding transcript records. This escalation linking enables targeted lecturer review and audit export queries.

Yes, this conversation logging approach supports learner-facing history by capturing every message and response within a structured schema. It enables learners to review their past interactions while providing lecturers with audit export capabilities.

The limitation of a 2-year retention policy for audit logs is that transcript entries are automatically managed and purged after this lifecycle period. You must extract necessary audit export queries and historical data before the retention window expires.