Automating repetitive work at OpenAI with Codex
Blog post from OpenAI
A software engineer describes using Codex and the open-source Runme notebook application to reduce repetitive operational work while retaining human oversight for important decisions. In the evaluation workflow, Codex reads a goal in a Runme notebook, develops a plan for approval, executes documented steps, and records commands, results, failures, and decisions so future runs can reuse the accumulated context. Runme combines Markdown, code, and HTML notebooks with Google Drive storage and searchable companion Markdown indexes, while WebMCP allows browser-based agent interaction without requiring a separate server-side MCP endpoint. The approach aims to consolidate information otherwise dispersed across terminal histories, chat systems, runbooks, and dashboards, making operational documentation easier to create during work and more useful for subsequent tasks. The author argues that this model lets AI handle routine execution while people remain responsible for approvals, tradeoffs, and consequential judgment, potentially reducing the burden of increasingly complex infrastructure tooling.
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