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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →An AI coding agent can take a well-scoped software task from a repository issue to a proposed change, but the developer still owns the plan, permissions, validation, and merge. A reliable workflow is to define what “done” means, ask for a plan when the task is substantial, choose whether the agent should work locally or in a hosted branch, then inspect and test the resulting diff before accepting it.
1. Turn the idea into a task the agent can verify
Start with the outcome, not a vague instruction such as “improve the settings page.” Name the affected behavior, constraints, and observable acceptance checks. For example: “Add a keyboard-accessible visibility toggle to the password field on the sign-in screen. Preserve the existing design, add or update tests for toggling visibility, and do not change authentication behavior.”
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A useful task description tells the agent what to change and how you will judge the result. Include relevant files or components if known, expected edge cases, and work that is explicitly out of scope. GitHub documents assigning a repository issue to Copilot and optionally adding prompt instructions; the issue should provide enough context for the agent to work toward a bounded result. GitHub’s guide to getting started with Copilot agents describes that workflow.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches- Outcome: What should a user or system be able to do afterward?
- Acceptance checks: What behavior, tests, or conditions demonstrate completion?
- Constraints: What must remain unchanged, and what patterns or dependencies should be respected?
- Scope: What is not part of this task?
2. Ask for a plan before implementation when the task needs one
For a small, clear fix, moving directly to a proposed change may be reasonable. For work spanning components, involving uncertain requirements, or carrying significant risk, first ask the agent to inspect the repository and outline its approach without editing files. GitHub recommends drafting an implementation plan first for larger or ambiguous tasks, and its cloud agent can research a repository and plan changes before writing code. See Using agent mode in your IDE and About GitHub Copilot cloud agent.
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A practical plan request—not a required vendor template—is: “Inspect the relevant code and tests. Do not edit yet. List the files likely to change, the steps you propose, the tests you would run, and any assumptions or questions that could change the implementation.”
Check whether the proposed steps actually satisfy the acceptance criteria. Correct misunderstandings, narrow unnecessary work, and resolve assumptions before authorizing implementation. A plan is a chance to catch a bad direction early; it is not proof that the eventual code will be correct.
3. Choose where the agent should work
The key distinction is where changes and commands happen, and when you review them. GitHub describes IDE agent mode as interactive work in a local development environment. Its Copilot cloud agent works independently in an ephemeral, GitHub Actions-powered environment and makes changes on a branch. These are different workflows, not interchangeable names for the same execution setting.
| Option | What the documentation describes | Useful when |
|---|---|---|
| IDE agent mode | Edits in a local development environment, streams proposed edits, and proposes terminal commands that the user can review and approve or reject. GitHub Docs | You want to watch, redirect, and review the work during a coding session. |
| Copilot cloud agent | Works in an ephemeral GitHub Actions-powered environment; can research and plan, change files on a branch, run tests and linters, and optionally create a pull request. GitHub Docs | You want to delegate a bounded issue and inspect the resulting branch or pull request afterward. |
Access to Copilot cloud agent is available on paid Copilot plans. For Business and Enterprise, administrator enablement affects availability, and repositories can opt out. Check the current product documentation and your organization’s settings before relying on access: Copilot cloud agent availability and details.
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4. Bound execution and manage permissions
In IDE agent mode, you can redirect the work as it proceeds and review proposed terminal commands before approving or rejecting them, unless execution is configured to happen automatically. In a cloud workflow, the agent operates in its hosted environment and produces a branch for review. Decide in advance what repository access, command execution, and external actions are appropriate for the task.
Sandboxing, configurable controls, and agent-aware telemetry are risk-management measures described in OpenAI’s Codex safety overview. They can help limit or observe activity, but they do not establish that generated changes are correct or harmless. Configure controls for the task and environment rather than treating an agent’s access as equivalent to a human’s judgment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Validate the checks and inspect the actual diff
Ask the agent to run the relevant tests, linters, or other project checks, and note exactly which commands ran and whether they passed. A green result is evidence only about those checks in that execution environment; it does not show that every requirement was met or that untested behavior is safe.
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Then review the changed files and the diff yourself. GitHub’s guidance is direct: “Now review the code changes yourself, just as you would for any contributor’s pull request.” GitHub Docs describes this review step.
- Compare the diff with the task and acceptance checks; look for unrelated edits or scope expansion.
- Check whether edge cases, error handling, and existing project conventions are addressed.
- Confirm that tests cover the changed behavior, not merely that a test command completed.
- Inspect commands or dependency changes for effects that the acceptance criteria did not call for.
6. Iterate, then accept deliberately
If the change is incomplete, give the agent specific feedback tied to the diff or acceptance criteria. GitHub documents requesting changes on the same branch or editing the branch yourself; once satisfied, you can approve and merge. OpenAI’s Codex app announcement describes reviewing agent changes in a thread, commenting on a diff, or opening changes in an editor. Introducing the Codex app.
Keep the acceptance decision with a human: request another revision, make the correction yourself, or approve and merge only after review. The agent can help produce and revise a proposed change; the person responsible for the repository remains responsible for deciding whether it is ready.
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