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For a substantial coding task, use an AI agent in a loop: define what “done” means, ask for a plan before large edits, keep the plan and constraints available, delegate only separable work, then inspect and test the changes before accepting them. This gives you review points at which to correct scope or implementation; it is a practical workflow, not a guarantee of better code.
How should you define a coding task for an AI agent?
Start with the desired change and the conditions that will make it complete. A useful prompt resembles a focused issue: describe the behavior or problem, the constraints, the relevant repository paths, and how you will check the result. Include relevant component names, code excerpts, diffs, or documentation where they help the agent understand the existing project. OpenAI’s Codex practice guidance recommends supplying concrete repository context and acceptance criteria rather than relying on a vague request.
- Change: State what should be added, corrected, or removed.
- Boundaries: Identify what should not change, along with compatibility or design constraints.
- Context: Point to relevant files, components, and project documentation.
- Acceptance checks: Specify the expected behavior and appropriate tests, linting, or type checks.
Clear acceptance checks are especially useful later: they give you a basis for deciding whether the implementation is complete instead of treating a plausible explanation from the agent as proof.
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For a large or uncertain change, yes: ask the agent to inspect the project and propose an implementation plan before it edits files. Planning creates a review point where you can catch a mistaken assumption, reduce scope, identify missing information, or change the sequence. OpenAI’s practice guidance recommends separating planning from the coding prompt for large tasks.
What to review in the plan
- Does it identify the relevant parts of the repository and the likely files to change?
- Does it state assumptions and open questions that could alter the implementation?
- Are the steps small enough to review, and do they account for compatibility or existing behavior?
- Does the plan include a way to verify the intended result?
GitHub documents a similar review-and-handoff workflow for Copilot IDE Plan mode. Its documentation says, “Plan mode helps you to create detailed implementation plans before executing them.” You can refine the plan and then hand it to the implementation agent; the documentation identifies the feature as a public preview, so availability and behavior may change. See GitHub’s Plan mode documentation.
A plan is not a substitute for examining the code or reviewing the finished changes. It is a way to expose decisions before implementation makes them more costly to unwind.
How do you keep an agent on track across a long task?
Keep durable context somewhere the next iteration can consult: the agreed scope, constraints, plan, decisions, and current status. OpenAI’s Codex practice guidance discusses using repository-level instructions such as AGENTS.md for persistent project context. Its guidance on longer tasks also describes recording the specification, plan, constraints, and progress in Markdown so they remain available as work continues.
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Keep these notes concise and current. Mark completed work and changed decisions rather than leaving an obsolete plan beside the implementation. This is a context-management practice, not evidence of a measured or guaranteed improvement in code quality.
When should you use multiple agents on one coding task?
Delegate when a piece of work is self-contained, has a clear output, and can proceed without repeatedly making cross-cutting decisions. For example, one agent might inspect a bounded component or summarize existing tests while the main session retains responsibility for integration and choices that affect the whole change. Avoid splitting tightly coupled edits merely to increase the number of agents: independently produced work still needs to fit together.
GitHub describes a subagent as working in its own context on a self-contained subtask and reporting back to the main chat. The documented wording is: “Delegate a self-contained subtask to a separate agent that works in its own context and reports back to your main chat session.” Codex CLI documentation also describes multi-agent tools for opening, reading, or forking tasks. These are documented capabilities, not evidence that more agents necessarily produce a better result. See GitHub’s agent documentation and Codex CLI documentation.
Make a delegation request actionable
- Name one bounded question or deliverable, not the whole project.
- Provide the relevant paths, constraints, and expected output.
- Say how the result should be returned, such as findings, a proposed change, or a reviewable patch.
- Keep integration decisions in the main session and inspect delegated work alongside the rest of the change.
How do implementation environments affect the workflow?
Some cloud coding workflows perform tasks in isolated workspaces with separate working files and changes. OpenAI’s Codex Cloud documentation describes this setup. Isolation can separate a task’s working changes, but it does not remove the need to understand what repository access, environment setup, permissions, or other controls apply to that product and task. Check those details before relying on a cloud run. See OpenAI’s Codex Cloud guidance.
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Implementation can proceed in manageable increments against the reviewed plan. If an environment cannot access a needed dependency, repository resource, or configuration, treat that as a constraint to resolve or work around—not as evidence that the code is complete.
How do you verify AI-generated code before accepting it?
Review the actual changes and run the checks that fit the project. OpenAI’s Codex Cloud guidance states, “Review the changes and test results before using the work.” A successful-looking response is not a substitute for examining the diff and reading test output.
- Inspect the diff. Check that the edits match the requested scope, do not introduce unrelated changes, and handle relevant edge cases.
- Run project checks. Use the applicable tests, linting, and type checks, following the repository’s documented commands and conventions.
- Read the results. Confirm what ran and whether it passed; investigate failures, skipped checks, or missing coverage instead of assuming success.
- Refine and repeat. If review or checks reveal a problem, request a targeted correction and inspect the updated diff and results.
- Accept only against the original criteria. Confirm that the outcome meets the conditions for done before using or merging it.
OpenAI’s long-task guidance gives an author’s example of running tests, lint, and type checks at milestones. That is a practitioner example, not a benchmark or universal requirement to run a particular suite at a particular interval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How does review work in a cloud-agent workflow?
In GitHub’s documented Copilot cloud-agent flow, research and planning are followed by changes on a branch; the user reviews the diff and decides whether to request more iteration or create a pull request. GitHub summarizes the sequence this way: “After research and planning, make changes on a branch, review the diff, and decide whether to iterate more or create a pull request.” The session does not create a pull request automatically. See GitHub’s cloud-agent workflow documentation.
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What can this workflow establish—and what can’t it?
Official product documentation can establish which planning, delegation, workspace, and review capabilities a product documents, as well as the intended steps in its workflow. It does not establish that plan mode, multiple agents, or any named workflow will improve outcomes in every project. The guidance cited here does not provide an independent head-to-head evaluation or quantified evidence that one approach produces better code than another.
When choosing a tool or workflow, compare concrete controls: whether it supports research before edits and a reviewable plan; how delegated work is returned; how repository context persists; what workspace isolation and access controls apply; and whether diffs and test results are visible before you use the changes. Product features, preview status, permissions, and cloud behavior can change, so consult current documentation for the specific edition and setup you use.
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