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An AI coding agent can take a software task from repository inspection through code changes and checks, but it works inside limits set by its tools, environment, and permissions. A typical workflow is to define the task, give the agent repository context, let it inspect and plan, review its implementation and test results, iterate on gaps, and preserve accepted work in source control. People still set the goal and decide whether the result is ready to ship.
How an AI coding agent moves through a development task
The following stages describe a practical workflow, not a guarantee that every agent or setup supports every step. OpenAI’s Codex materials provide specific examples; other products may differ in their tools, permissions, and workspaces.
1. Define the task and how to judge it
Give the agent a bounded outcome, relevant constraints, and acceptance criteria. A focused task might be investigating a bug, changing a named component, or adding a behavior with a defined test. For a substantial change, ask for a plan before implementation. Relevant file paths, diffs, or documentation can give the agent useful context. OpenAI’s Codex CLI best practices recommend focused tasks and planning for larger changes.
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2. Prepare the repository and working environment
The agent needs access to the code and the tools required for the task. What it can do depends on where it runs and how that environment is configured. In a local command-line workflow, it works with the developer’s local repository and installed tools. In Codex Cloud, an environment groups repositories, dependencies, tools, and access settings. See Codex Cloud environments and Codex CLI for those product-specific models.
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3. Inspect the codebase and plan the change
The agent explores relevant files and tries to identify where the change belongs. For a large or unfamiliar task, reviewing its proposed implementation plan before it edits code gives the developer an opportunity to correct misunderstandings early. The plan is a proposal to assess, not evidence that the agent has fully understood the system.
4. Make code changes within the available permissions
Once the approach is clear, the agent can edit files or prepare a patch, subject to its access and permission model. A local task operates against the local repository; a Codex Cloud task uses an isolated workspace. Permission settings determine whether an agent can only suggest changes, edit files, execute commands, or interact with review artifacts. The exact capabilities depend on the product and setup; OpenAI describes Codex permission controls in its CLI features documentation.
5. Run checks and report what happened
Depending on the environment, an agent may run project tests or other command-line checks. More instrumented setups can also expose an application interface, logs, and metrics for inspection. A test passing establishes that the particular check passed under the conditions in which it ran; it does not prove the whole change is correct or production-ready. Report the checks that actually ran and their results rather than describing the software as simply “tested.”
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6. Review the diff and iterate
Review the actual code changes alongside the test output. If the implementation misses a requirement, provide specific feedback and ask for a correction. Some workflows support continuing the same task through multiple edits and review passes. OpenAI’s engineering account describes iterative agent review, while its cloud guidance says to review the changes and test results before using the work. The human reviewer remains responsible for deciding whether the changes meet the acceptance criteria.
7. Preserve and hand off accepted work
Once changes are accepted, keep them in source control and hand them off through a pull request or equivalent review process. Git checkpoints around tasks make it easier to identify and recover work. In Codex Cloud, tasks use isolated workspaces, and a new task does not automatically recover another task’s uncommitted changes; OpenAI advises committing important work. See the CLI best practices and Codex Cloud help.
What determines how much of the workflow the agent can handle?
The agent’s practical reach depends less on the word “agent” than on whether its working environment makes the task possible and verifiable. When assessing a workflow, check these dimensions:
- Where the work runs: A local machine and an isolated cloud workspace have different repository and environment boundaries.
- What it can access: Files, dependencies, development tools, and connected services must be available and appropriately configured.
- What permissions it has: Suggesting a patch, editing files, running commands, and updating review artifacts are distinct capabilities.
- What can be validated: A setup may support command-line tests alone, or also expose the running application, logs, and metrics.
- How work is reviewed and retained: Check task continuity, diffs, review steps, source-control checkpoints, commits, and pull requests.
In an OpenAI engineering account, the company described early work being slowed by an underspecified environment, then adding repository knowledge, tests, guardrails, application access, and observability. That is a company case study, not an independent evaluation or a universal recipe. The broader lesson is practical: an agent cannot reliably complete or verify work that its environment does not expose.
How teams coordinate many agent tasks
A single developer can use the task-by-task loop without an orchestration system. For teams handling many tasks, a tracker can serve as a queue and coordination layer. OpenAI’s Symphony article describes mapping open Linear issues to agent workspaces, waiting for dependencies to clear, and having people review the results. This is one scaling pattern, not a requirement for using an agent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What productivity figures can—and cannot—tell you
OpenAI has published internal figures from its own work, but these should be read as organization- and period-specific reports rather than forecasts for other teams.
- In its Harness Engineering account, OpenAI reported roughly 1,500 pull requests opened and merged over five months, averaging 3.5 pull requests per engineer per day for a three-engineer team. The account says the team later grew to seven engineers and throughput increased.
- In its Symphony article, OpenAI reported a 500% increase in landed pull requests on some teams during the first three weeks of an internal rollout.
These company-published examples do not establish an industry-wide typical productivity gain. They also do not show what another team should expect from a different codebase, task mix, environment, or review process.
What a sound handoff looks like
A useful handoff makes the work inspectable rather than asking a reviewer to trust a summary. Include the change, the checks that actually ran, and any unresolved issue that affects acceptance. The reviewer can then compare the implementation with the task criteria and decide whether to request another iteration, accept the work, or block it.
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