OpenAI released a detailed technical breakdown by Codex team engineer Michael Bolin outlining the internal Codex CLI coding agent and its iterative agent loop. The post explains how the tool writes code, runs tests, and fixes issues under human supervision.
The writeup places Codex in a broader trend of AI agents and notes design philosophy guiding the Codex product as AI assistants become more practical for prototyping and production work.
OpenAI cautions that these tools are not error-free: while they can be fast on simple tasks, they are brittle beyond their training data and require human oversight for deployment.
Bolin highlights concrete engineering challenges, including the quadratic growth of prompts, cache-miss related performance issues, and bugs such as inconsistent enumeration of MCP tools, all of which the team had to fix.
Unlike some products, OpenAI has seldom published such in-depth internal breakdowns for ChatGPT; Codex tasks appear particularly well-suited to large-language-model approaches and the agent loop model.
