Codex Task Execution Model: Evolving from Code Generation to Reviewable Software Engineering Agents

In May 2025, OpenAI first launched Codex as a cloud-based research preview, allowing it to read code repositories, write features, fix bugs, answer codebase queries, and generate reviewable Pull Requests in an isolated cloud environment. By October 2025, Codex entered general availability, integrating SDK support, Slack integration, and enterprise management capabilities.

The year 2026 marked further expansion: Codex App was released in February, bringing multiple agents, parallel tasks, Worktree, and long-term task management to desktop environments, with Windows support added in March. In July 2026, Codex App was merged into the new ChatGPT desktop application, retaining its role as a software engineering agent while gaining multi-repository project support, in-Diff editing, and Pull Request review features. OpenAI revealed in May 2026 that Codex had over 4 million weekly active users.

This evolution reflects core shifts in AI coding tools over the past two years. Early code assistants focused on generating code snippets, using context from the current file and cursor position to produce completions. Codex, however, now operates at the level of "engineering tasks"—developers can submit requests such as fixing a concurrency bug, refactoring an authentication module, analyzing request chains across a repository, or adding tests and checking for regressions. Codex then reads project context, uses tools like file access, Shell commands, and others in a controlled environment to modify code, run validations, and deliver Diffs, summaries, or Pull Requests for developer review. It is currently available across ChatGPT, IDEs, CLI, and cloud environments, with extensions like Worktree, Skills, MCP, automated tasks, and code review.

The key question surrounding Codex is: How does a model transition from "generating text" to "executing software engineering tasks"? This article delves into its task execution model.

### Product Definition
Codex functions as an agent for software engineering environments. While large language models handle understanding natural language, code semantics, and task objectives, Codex provides access to engineering tools like code repositories, file systems, Shell, Git, networks, and MCP, managing their execution environments, permissions, and result presentation. This gives the model continuous action capabilities.

For example, if a developer asks to "fix the order creation interface that generates duplicate orders on repeated requests and add regression tests," a standard chat model might suggest code changes, but Codex goes further: it reads the order Controller, Service, Repository, and database definitions, searches existing tests, modifies relevant files, runs test commands, adjusts code based on results, and finally submits actual code changes as a Diff for review. This involves multiple model inferences and tool calls, so tasks typically go through several execution steps.

The "Agent" concept here refers to a loop: the model chooses the next action based on current state, tool execution results feed back into the context, and the model continues until the task is complete. Since this loop can have real-world effects (file changes, test runs, Git Diff), Codex addresses issues like execution isolation, permissions, validation, and human review.

Codex's structure can be broken into five parts (for understanding public mechanisms): Model (understands tasks and code), Context (user instructions, code repos, AGENTS.md, Skills), Tools (file operations, Shell commands), Execution Environment (local, Worktree, cloud), and Human Control (permissions, Diff checks, reviews). Together, these turn natural language requests into actionable engineering tasks.

### Task Model
When Codex receives a user request, it first builds sufficient task context. Code development context extends beyond the user's input—for example, modifying a Spring Boot interface may involve Controller, Service, Repository, DTO, config files, and tests, which Codex gathers via code search and file reading.

Codex uses AGENTS.md, a project guide file for agents that teams can use to store build commands, test methods, code standards, directory conventions, and review requirements. This solves the problem of long-standing engineering rules not being in the user's prompt. For instance, a repo might specify Maven build commands, test rules, architecture constraints (e.g., keep business logic out of controllers), and review checks (e.g., exception paths and null handling).

Skills further handle reusable work methods—they encapsulate instructions, resources, and scripts for repeated tasks like pre-release checks or database migration validations. While AGENTS.md is for long-term repo rules, Skills are for repeatable task capabilities.

### Execution Loop
A key part of Codex is the Agent Loop, mentioned in OpenAI's Codex GA announcement. The Codex SDK inherits the CLI's Agent implementation, including Prompt, Tool Definitions, and Agent Loop. This loop involves the model deciding actions, tools executing them, results feeding back into context, and repeating until the task is done.

Sources

• CSDN Blog Post: "Codex Task Execution Model: From Code Generation to Reviewable Software Engineering Agents" (URL: https://blog.csdn.net/qq_65052774/article/details/163747218)