An AI coding agent can work through a development task—from inspecting a repository to changing code and running checks—but it does so within the limits of its environment and permissions. The practical workflow is a loop: define the task, provide context, inspect and plan, implement, validate, review, iterate, and preserve accepted work. People remain responsible for setting the goal and deciding whether the result is ready to ship.
How an AI agent moves from a request to a code change
The following steps describe a general workflow, with OpenAI Codex documentation as a concrete example. Details such as where work runs and which actions are permitted vary by product and setup.
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Define a bounded task
Describe the outcome, constraints, and acceptance criteria. A focused request might ask the agent to investigate a specific bug, change a named component, and run the relevant project tests. For a larger change, begin with a plan and break the work into manageable tasks. Relevant file paths, diffs, or documentation can provide useful context. OpenAI’s CLI practice guide recommends focused tasks and planning for larger changes.
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Prepare the repository and environment
The agent needs access to the code and to the tools and dependencies required for the task. In Codex Cloud, an environment bundles repositories, tools, dependencies, and access settings; in local CLI work, the agent uses tools installed on the developer’s machine. The environment therefore determines what the agent can inspect and do. OpenAI’s CLI guide and its Codex Cloud help page describe these different setups.
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Inspect the code and plan the change
The agent explores the repository to locate the relevant code and understand how the requested change fits. For substantial work, asking for a proposed implementation plan before editing can reveal misunderstandings early. The developer still needs to judge whether the plan fits the project’s requirements.
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Implement within the available permissions
Depending on its setup, an agent may suggest edits, modify files, or run commands. In the documented Codex examples, cloud tasks work in separate workspaces, while local CLI tasks operate against the local repository. The allowed actions depend on the product’s permission model and configuration—not on the word “agent” alone.
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Run checks and report what happened
The agent may run tests and other development tools available in its environment. More instrumented setups can also expose an application’s UI, logs, or metrics for checking. A test passing is evidence about that test under the conditions in which it ran; it does not establish that the whole change is correct or production-ready. The handoff should state which checks actually ran and their results.
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Review, correct, and repeat
Inspect the diff as well as the test results. If the code misses a requirement or introduces a problem, give specific feedback and ask for a correction, then run the relevant checks again. OpenAI’s engineering account describes review loops, while its Cloud guidance tells users to review changes and test results before using the work.
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Preserve accepted work
Once the change is acceptable, keep it through the project’s normal source-control process: for example, a commit and pull request or an equivalent review artifact. Codex Cloud tasks are isolated; a new task does not automatically recover another task’s uncommitted changes. OpenAI’s Cloud help page advises committing important work, and its CLI guide recommends Git checkpoints around tasks.
What the environment changes
An agent’s ability to complete the loop depends partly on how well its development environment supports the work. In an account of its internal engineering effort, OpenAI says early work was slowed by an underspecified environment; it describes adding repository knowledge, tests, guardrails, application access, and observability. This is a company case study, not independent evidence that every team needs the same setup or will see the same results. OpenAI’s account explains the changes it made.
For an individual task, a local CLI and a cloud workspace are two different arrangements to assess—not a universal ranking of agent products. The key questions are where the work runs, what it can access, what actions it may take, what checks are possible, and how changes are reviewed and retained.
- Work location: local machine or isolated cloud workspace.
- Available context and tools: repository files, dependencies, configured tools, and connected services.
- Permissions: whether the agent can suggest changes, edit files, execute commands, or update review artifacts.
- Validation: command-line tests alone, or additional access to the running application, logs, and metrics.
- Review and continuity: access to diffs and results, task continuity, review steps, and source-control checkpoints.
OpenAI’s documentation supports a comparison of its local CLI and Codex Cloud workflows, but it does not establish a neutral feature comparison across coding-agent vendors.
Best Value
What published productivity figures do—and do not—show
OpenAI has published figures from its own internal projects. They illustrate what the company reported in particular settings; they are not forecasts for another team or a measure of typical industry-wide gains.
| Published report | Reported figure | Scope |
|---|---|---|
| OpenAI, Harness Engineering | Roughly 1,500 pull requests opened and merged over five months; an average throughput of 3.5 pull requests per engineer per day. | A three-engineer team in the internal project described. The article says the team later grew to seven engineers and throughput increased; the figure is not a general benchmark. |
| OpenAI, Symphony | A 500% increase in landed pull requests on some teams during the first three weeks. | OpenAI’s account of its internal rollout; the claim is specific to some teams and that initial period. |
These company-reported examples do not establish what another organization should expect. The cited accounts provide no independent industry-wide benchmark for typical productivity gains.
When teams coordinate many agent tasks
A task tracker can also serve as a queue or control plane when a team is coordinating work at scale. In OpenAI’s Symphony account, open Linear issues are mapped to agent workspaces, dependencies are allowed to clear, and people review the results. This is one orchestration pattern for managing many tasks; it is not a prerequisite for using an agent on an individual change.
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The agent can perform work inside the boundaries it is given, but the workflow still needs a person to set intent, supply relevant context, assess whether acceptance criteria have been met, and decide whether the change should be used. That review should consider both the code and the evidence from checks. The appropriate level of scrutiny depends on the task and the project’s own review practices; a passing test alone is not a blanket guarantee.
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