Training Data Audit with Codex: API Runtime Pattern
A production playbook for training data audit in cross-industry operations using Codex: api runtime pattern, run-scoped inputs, logs, typed results, and artifacts.
Audience: AI governance teams
The problem
AI governance teams need training data audit to run repeatedly against datasets, consent records, filters, and model usage notes. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.
Implementation path
Package the training data audit instructions as a skill, send datasets, consent records, filters, and model usage notes as run-scoped inputs, execute with Codex, poll terminal status, and consume argo.result.v1 instead of parsing a transcript.
Tradeoffs and failure modes
The API boundary forces the workflow to define inputs, terminal states, and result shape before customers depend on it. For training data audit, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.
Run request
POST /api/skills/<skill_id>/run
provider=codex
workflow=training-data-audit
inputs[]=@./input-pack.zip
result_schema=argo.result.v1
Run this on Argo