ETL Error Diagnosis with Codex: API Runtime Pattern
A production playbook for ETL error diagnosis in cross-industry operations using Codex: api runtime pattern, run-scoped inputs, logs, typed results, and artifacts.
Audience: Data platform teams
The problem
Data platform teams need ETL error diagnosis to run repeatedly against pipeline logs, schemas, samples, and freshness checks. 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 ETL error diagnosis instructions as a skill, send pipeline logs, schemas, samples, and freshness checks 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 ETL error diagnosis, 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=etl-error-diagnosis
inputs[]=@./input-pack.zip
result_schema=argo.result.v1
Run this on Argo