🤯Coding AI Agents: The Secret Explained! 🚀

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OpenAI’s Codex CLI: Unveiling the “Agent Loop”
OpenAI engineer Michael Bolin recently published a comprehensive technical breakdown of the company’s Codex CLI coding agent, offering developers invaluable insight into the inner workings of AI coding tools – including their ability to generate code, run tests, and fix bugs with human guidance. This release complements previous coverage of AI agents, detailing the specific technical implementation of OpenAI’s “agentic loop.”

AI Coding Agents Surge in Popularity
Currently, AI coding agents are experiencing a significant surge in popularity, driven by tools like Claude Code with Opus 4.5 and Codex with GPT-5.2. These tools are demonstrating a notable advancement in their utility for rapidly generating prototypes, interfaces, and boilerplate code, reflecting the growing recognition of AI agents as practical tools for everyday work.

The “Agent Loop”: A Technical Deep Dive
Bolin’s post centers on what he describes as “the agent loop,” representing the core logic that orchestrates interactions between the user, the AI model, and the software tools the model utilizes during coding work. This agent loop involves a repeating cycle: the agent receives input from the user and prepares a textual prompt for the model, which then generates a response – potentially requesting a tool call, such as running a shell command or reading a file. If a tool call is requested, the agent executes it, appends the output to the original prompt, and queries the model again, continuing until the model ceases requesting tools and produces an assistant message.

Codex’s API Design and Zero Data Retention
Codex operates without an optional “previous_response_id” parameter, meaning each API request is fully stateless – it transmits the entire conversation history with every call, rather than the server retrieving it from memory. This design choice simplifies API support and facilitates the implementation of “Zero Data Retention,” where OpenAI does not store user data.

Prompt Optimization and Mitigation Strategies
The increasing length of prompts directly impacts performance due to the limitations imposed by the context window. Bolin notes that Codex automatically compacts conversations when token counts surpass a defined threshold, mirroring a similar function in Claude Code. To avoid cache misses – triggered by operations like changing available tools or modifying the sandbox configuration – Codex must carefully manage prompt length and execution.

Enhancements and Future Developments
Codex utilizes a specialized API endpoint to compress the context while simultaneously preserving summarized portions of the model’s understanding through an encrypted content item. Bolin indicates that subsequent posts in his series will delve into the architecture of the command-line interface, the detailed implementation of the tools, and Codex’s sandboxing model.

This article is AI-synthesized from public sources and may not reflect original reporting.