Data Extraction Quality Audit with Codex: API Runtime Pattern
A production playbook for data extraction quality audit in cross-industry operations using Codex: api runtime pattern, run-scoped inputs, logs, typed results, and artifacts.
Audience: AI operations teams
The problem
AI operations teams need data extraction quality audit to run repeatedly against extracted fields, source docs, validation failures, and review 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 data extraction quality audit instructions as a skill, send extracted fields, source docs, validation failures, and review 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 data extraction quality 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=data-extraction-quality-audit
inputs[]=@./input-pack.zip
result_schema=argo.result.v1
Run this on Argo