Human Review Queue Preparation with Codex: API Runtime Pattern
A production playbook for human review queue preparation in cross-industry operations using Codex: api runtime pattern, run-scoped inputs, logs, typed results, and artifacts.
Audience: Operations teams combining automation with review
The problem
Operations teams combining automation with review need human review queue preparation to run repeatedly against agent output, confidence notes, artifacts, and escalation policy. 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 human review queue preparation instructions as a skill, send agent output, confidence notes, artifacts, and escalation policy 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 human review queue preparation, 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=human-review-queue
inputs[]=@./input-pack.zip
result_schema=argo.result.v1
Run this on Argo