> ## Documentation Index > Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Export Audit Packet > Download a completed Improve cycle audit packet as JSON for records, automation, or external AI review When an Improve cycle reaches review, Adaline shows an **Audit packet** section near the bottom of the review page. It summarizes how the cycle was triggered, what candidate set was explored, which candidate won, and what evidence contributed to the result. Improve audit packet section with export button, selection process, stage provenance, and execution timeline ## When to export Export the packet when the cycle needs to leave the review UI. | Use case | Why it helps | | --------------------------------- | ---------------------------------------------------------------------------------------------------- | | **Audit record** | Keep a durable copy of what Adaline reviewed before a candidate was approved, edited, or rejected. | | **External AI review** | Send the packet to an external AI agent that can inspect the evidence and return a recommendation. | | **No-human-in-the-loop workflow** | Let another system decide what to do next while keeping Adaline as the source of the cycle evidence. | | **External deployment path** | Hand off the cycle context to a release system that does not deploy directly through Adaline. | ## What is in the JSON The export is the machine-readable version of the audit packet section. It includes user-relevant structured data and excludes raw internal optimizer state. | Area | What it contains | | ---------------------- | ------------------------------------------------------------------------------------------------------ | | **Export metadata** | The time the packet was exported. | | **Cycle context** | Trigger, focus note, and cycle-level review context. | | **Results** | Candidates explored, winner count, total candidates, and pass/reject rates. | | **Selection process** | Rows that explain how candidates were filtered or selected. | | **Stage provenance** | One summary line for Behaviors, Datasets, Evals, Prompts, and Review. | | **Execution timeline** | Chronological milestones from cycle start through ready-for-review, with stage details when available. | This makes the packet useful for software, not only humans. An external reviewer can parse the same evidence path the UI shows, compare candidate selection and stage provenance, and decide whether the cycle is ready to approve, edit, rerun, or reject. ## Use with external AI agents An external AI agent can read the audit packet and return a structured recommendation for your workflow. Common checks include whether the trigger matches the intended issue, whether enough candidates were explored, whether regressions were filtered, whether the provenance is credible, and whether the cycle is ready for deployment through Adaline or an external release path. Exporting the packet does not approve, reject, edit, or deploy the cycle. It only gives another system the review context it needs to make or explain a decision. ## Before sharing externally Audit packets can include prompt context, production-derived behavior labels, evaluator and dataset summaries, timeline details, and project metadata. Review your data-sharing policy before sending the JSON outside your environment. Remove or mask any information your external system should not receive, especially customer identifiers, private metadata, credential values, raw IDs, or sensitive production examples. Understand the review page that contains the audit packet. See how Adaline explores and scores candidate prompt changes.