OpenAI's Codex agent loop deep dive
This OpenAI post is a useful technical read because it explains the agent loop, context construction, model calls, and tool orchestration behind Codex.
Source
Unrolling the Codex agent loop
Why I saved it
I like this kind of article because it goes below the product surface. It explains the harness, not only the model.
For coding agents, the harness is everything around the model: prompt construction, context, tools, command execution, state, and final reporting.
My notes
- Codex is described as a suite, with the CLI harness as the focus of this article.
- The core loop is about preparing instructions, calling the model, running tools, and feeding results back.
- The Responses API endpoint is configurable, which makes the loop portable across compatible endpoints.
- Good agent behavior depends on how context is prepared and updated, not only on raw model intelligence.
What I want to remember
If I build a coding CLI or agent, I should design the loop as a first-class product. Prompts, tools, permissions, tests, and context management should be code, not hidden magic.