What an audit-ready AI handoff actually contains
The handoff begins with the boundary
The first page should state what the system owns and what it does not. Which sources can it read? Which systems can it write to? Which actions require approval? Which classes of request are refused or escalated? Ambiguous boundaries become production incidents because operators assume the system has a control that was only discussed in a meeting.
Evaluation results need a version and a decision
A quality score without the test set, evaluator version, threshold, and decision is not reproducible. The handoff records what was tested, the failure categories, the accepted thresholds, and who approved any exception. When the model changes, the receiving team can run the same suite and compare the result instead of trusting a new demo.
- Test cases
- 600+ graded conversations
- Critical failures
- 0 release threshold
- Replay coverage
- 100% decisions traceable
- Ownership gaps
- 0 named by control
Ownership is a control, not a line in an org chart
Every important behavior needs an owner: model change approval, knowledge source freshness, eval maintenance, incident triage, access review, and rollback. The owner is the person who can make the decision or escalate it, not the person who attended the build review. If no one owns the control, the control is aspirational.
Frequently asked
What documents belong in an AI handoff?
System boundary, architecture and data flows, model and prompt versions, evaluation suite and results, guardrails, access model, ownership matrix, incident and rollback procedures, operating runbook, and replay instructions. The receiving team should be able to operate and explain the system without relying on undocumented context from the build team.
How often should the evaluation suite run?
Whenever the model, prompt, retrieval corpus, policy layer, or downstream action changes. The suite should also run on a recurring cadence against the production configuration so silent vendor model changes and knowledge drift do not go unnoticed. The important part is versioned comparison against declared thresholds.
What makes an AI handoff audit ready?
A reviewer can trace a decision from input through retrieved context, model and policy version, downstream action, approval, and final outcome. They can also identify the owner of each control and replay a representative case. Audit readiness is evidence and repeatability, not the presence of a governance slide deck.
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