AI Decision Traceability Log
Record what the AI saw, retrieved, inferred, recommended, and what humans finally decided.
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The exact sections inside the download.
Frame the operating decision
What user request or workflow trigger started the decision? Which sources, records, or memories were used? Use the combined view to name the business outcome, source of truth, and owner who will decide whether to proceed.
Set the control boundary
What did the model infer beyond the raw source material? Who reviewed the recommendation and what evidence did they inspect? State the risk to avoid, review or escalation route, and record needed to keep the workflow controlled.
Commit to the first move
What final decision, message, task, or tool action occurred? What identifier links the request, output, review, and final action? Record the smallest safe scope, baseline, owner, evidence, decision date, and next action.
Decision summary
What identifier links the request, output, review, and final action? If changed, why did a human override the AI? Summarize the proposed move, operating benefit, and the evidence that supports it.
Evidence and control
Who may need to inspect this later? State the source, review boundary, accountable owner, and condition that would require revision or pause. Keep the decision receipt with the workflow record.
Next review
Record the decision owner, evidence source, review date, and the explicit condition to scale, revise, defer, or stop. Confirm how the resulting decision will be communicated, implemented, and retained in the operating record.
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Frame the decision
Name the real operating need before designing a solution.
Name the recommendation, classification, approval, or escalation.
List records, documents, tools, and memory used.
Name who accepted, changed, rejected, or overrode the recommendation.
How to use it
Start with one real decision.
Complete the canvas with the workflow owner, then use the blank areas to expose missing context and controls.