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For substantial coding work, use an iterative loop: define what done means, ask the agent to inspect and plan before editing, keep the plan and project context available, delegate only separable tasks, then review the changes and run relevant checks before relying on them. This creates clear human review points; it is a practical workflow, not a guarantee of better code.
How should I use plan mode for coding?
Use plan mode when a change is large, touches unfamiliar areas, or has meaningful trade-offs. Treat its output as a proposal to inspect—not as permission to start coding. GitHub describes IDE Plan mode as a way to research a task and draft a plan for review before code changes; its documentation labels the feature public preview, so availability and behavior may change.
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GitHub Docs puts the purpose plainly: “Plan mode helps you to create detailed implementation plans before executing them.” The useful part is the review step: you can catch mistaken assumptions, clarify scope, and decide what should happen before implementation begins.
A practical planning sequence
- Describe the outcome. State the behavior or change you want, the constraints that matter, relevant repository paths, and how you will judge completion. A prompt shaped like a useful issue or pull request description gives the agent concrete context.
- Ask for inspection before edits. Request that the agent examine relevant code, identify assumptions and open questions, and propose an actionable sequence without changing files.
- Review and revise. Check that the steps address the actual requirement, fit the codebase, and include any necessary tests or documentation. Answer questions and narrow or correct the scope.
- Hand off for implementation. Once the plan is workable, ask the agent to implement it. For a large change, keeping planning and coding as distinct stages makes the point of human review explicit.
A plan is most useful when it names concrete areas of work and checks, not when it merely restates the request. If the task is tiny and unambiguous, planning can be a brief check rather than a separate ceremony.
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How do I keep a coding agent on track across a long task?
Keep durable context somewhere the agent and the people working on the repository can revisit. Record the goal, decisions, constraints, plan, and current status in an appropriate project document. OpenAI’s practice guidance recommends clear structure and relevant repository details, including persistent AGENTS.md context; its long-horizon guidance describes keeping the specification, plan, constraints, and status in Markdown.
Make these notes specific enough to resolve ambiguity. For example, record which behavior must remain unchanged, which paths are in scope, what decisions have already been made, and which checks remain. Update the status when the plan changes so a later iteration does not treat an outdated assumption as current. This is a recommended practice, not a documented guarantee of improved outcomes.
When does multi-agent delegation make sense?
Delegate when a subtask is self-contained, has a clear expected output, and can be reviewed independently. Keep integration, shared design choices, and cross-cutting decisions in the main session. GitHub documents subagents as working in their own context and reporting back; Codex CLI documentation also describes tools for agents to open, read, or fork tasks. These are workflow capabilities, not proof that adding agents always improves quality.
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GitHub Docs describes the handoff this way: “Delegate a self-contained subtask to a separate agent that works in its own context and reports back to your main chat session.” That model works best when the main task can specify the boundary and later assess the result in context.
Write a bounded delegation request
- Task: Name one deliverable, such as identifying relevant tests or implementing a separate, well-defined component.
- Boundary: Specify what files or decisions are in scope and what must remain untouched.
- Output: Ask for a concrete result, such as a proposed change, findings with file references, or a summary of relevant risks.
- Return path: Require the agent to report what it did, what it found, and any uncertainties to the main session.
Do not delegate tightly coupled changes without a clear integration plan. Separate contexts can reduce shared clutter, but they also mean the main session must reconcile decisions and inspect the combined result.
How should implementation proceed?
Use the reviewed plan as a guide and work in increments that are easy to inspect. After each meaningful unit, compare the implementation with the intended behavior and update the recorded status if scope or assumptions have changed.
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In a cloud-agent workflow, check the environment and access model before starting: repository permissions, setup requirements, and product-specific controls can affect what the task can do. OpenAI documents Codex Cloud tasks as running in separate isolated workspaces with separate working files and changes. Isolation describes workspace behavior; it does not remove the need to review access or the resulting code.
How do I verify AI-generated code?
Before accepting or using a change, inspect the diff and run the checks that make sense for the project. Depending on the repository, those may include tests, linting, type checks, or other documented validation. Read the output: an agent’s claim that a check passed is not a substitute for seeing what ran and whether it succeeded.
Best Value
- Review the diff. Check that changes are within scope, match the plan, and do not introduce unrelated edits.
- Run relevant checks. Use the project’s established commands and environment. Include tests or static checks that cover the changed behavior where available.
- Investigate failures and gaps. Determine whether a failure comes from the change, the environment, or an unrelated existing issue. Ask for a targeted fix or explanation, then inspect the revised diff.
- Decide whether it is ready. Accept or hand off the work only when it meets the stated completion conditions and you understand any remaining limitations.
OpenAI Help Center’s Codex Cloud guidance says: “Review the changes and test results before using the work.” OpenAI’s long-horizon article also gives a practitioner example of running tests, lint, and type checks at milestones; that example is not a benchmark or a universal test schedule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I review before handing off or merging?
Use the original completion conditions as the decision point. If the implementation misses one, continue iterating; if it meets them, hand it off or proceed through the repository’s normal review and merge process. In GitHub’s documented cloud-agent flow, the user inspects the diff and chooses whether to request refinements or create a pull request; the session does not create a pull request automatically.
GitHub summarizes that workflow as: “After research and planning, make changes on a branch, review the diff, and decide whether to iterate more or create a pull request.” The key is that implementation, review, and acceptance are separate decisions.
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Compare what each workflow lets you control and inspect rather than inferring code quality from feature lists. The cited product documentation describes capabilities and intended use; it does not provide an independent head-to-head evaluation or quantified evidence that one approach produces better results.
- Planning: Can it inspect first, surface open questions, and produce a plan you can review before edits?
- Delegation: Can work be split into self-contained tasks, and how do results return to the main session?
- Context continuity: Can repository details, plans, constraints, and progress remain available between iterations?
- Isolation and access: Does work run in a separate workspace, and what repository, network, credential, or administrator controls apply?
- Verification visibility: Can you inspect the diff, test results, and refinements before using or merging the work?
Features and availability can change, particularly where documentation marks a capability as preview. Check the current documentation for the product and environment you use before relying on a specific control or workflow.
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