artifacts

To track AI agent generated files across stateless sessions, you can use a persistent artifact ledger to register raw files, metadata, metrics, and lineage for every generated output. This ensures outputs like reports and images are not lost.

The best way to audit lineage and lifecycle states for AI generated artifacts is to use a persistent ledger that tracks file provenance and cross-object relationships. It records status transitions from pending to completed or archived.

Yes, you can attach generated reports to specific tasks or sessions for later retrieval. By registering the file in the artifact ledger with a title, you create a structured tracking record for easy audit and retrieval.

Yes, artifact ledger management works within the Ravi runtime environment. It facilitates the auditing and retrieval of outputs across sessions, tasks, and projects by maintaining structured tracking of file provenance and metrics.

To create a new record for a file in the ledger, you use the artifact skill to register the file located at a specific path with a designated title. This attaches the output to your generation history for tracking.

You lose track of file provenance and metrics because stateless sessions do not retain output history. Using a persistent artifact ledger solves this by centrally registering metadata, metrics, and lineage for every generated output.