OpenAI published a technical post outlining how the Codex agent loop operates, offering a detailed look at the core system behind Codex CLI. The article, released in January 2026, was written by Michael Bolin, a member of the company’s technical staff.
The post explained that the agent loop is responsible for coordinating user input, model inference, and tool execution.
Codex repeatedly sends prompts to a language model, interprets responses, executes requested tools such as shell commands, and feeds the results back into the model until a final assistant message is produced.
OpenAI described how Codex builds prompts using structured roles, including system, developer, user, and assistant messages.
Each conversation turn appends new information to the prompt, allowing the agent to reason over prior actions while continuing the task.
The article also detailed how Codex interacts with the Responses API, supports streaming outputs, and handles tool calls through structured JSON events. Each turn ends with an assistant message, signaling that control returns to the user.
Why This Matters Today
The explanation provides insight into how modern AI coding agents operate beyond simple text generation.
If you use agent-based tools, the post clarifies how iterative reasoning, tool execution, and state management are combined to perform real software work.
OpenAI also addressed performance tradeoffs. Because prompts grow with each turn, Codex relies on prompt caching and context compaction to avoid excessive computation and context window limits.
The company said it avoids stateful optimizations that would conflict with Zero Data Retention configurations.
The post signals OpenAI’s intent to be more transparent about agent design as interest in autonomous and semi-autonomous AI systems increases. It is the first in a planned series covering Codex architecture, tool usage, and sandboxing.
Our Key Takeaways:
- OpenAI published a detailed technical explanation of how the Codex agent loop coordinates model inference and tool execution.
- The post outlined how prompts grow over time and how Codex manages performance through caching and context compaction.
- OpenAI said future posts will cover Codex architecture, tool integration, and sandboxing in more detail.
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