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To find out whether an AI coding agent stayed within your task, compare the complete diff with the original request—not just the final summary or test results. Review scope, correctness, and security as separate questions, and treat logs as clues rather than proof that every edit was authorized. The official guidance reviewed here describes manual review and traceability features, not a universal automated prompt-to-diff scope score.
Keep the original request as your review standard
Before judging the code, identify what the task actually asked for. Pull out the intended outcome, explicit constraints, and acceptance checks. Constraints might specify files or behavior that must remain untouched; checks might name a test command or expected result.
Microsoft’s Visual Studio Code best practices recommends including relevant files, errors, constraints, and existing test commands in a task. Its example, “Limit changes to the existing view and its tests,” makes the scope boundary explicit. If the request is still available in the agent’s conversation, keep it open while reviewing; otherwise, use the version saved in your issue, prompt record, or task description.
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Use the agent’s changes view, a unified diff, Source Control, or the pull request to see the full change set. Read file names as well as code: a new configuration file, dependency, endpoint, or unrelated documentation edit can matter even when the main feature looks right.
#1 Best Overall
The Visual Studio Code review and revert guide notes that Agent Host changes may already be saved in a folder or isolated worktree, so review them in a diff before committing or integrating them. Include untracked files in your review; a diff that omits them is not a complete account of what the agent produced.
Trace each consequential edit to the task
For each changed file—and each consequential edit within it—ask:
- Which requested outcome does this support?
- Is this edit necessary to deliver that outcome, or is there a narrower way to do it?
- Does it contradict an explicit constraint or introduce unrelated behavior, dependencies, configuration, or endpoints?
These questions are a practical review method, not a published standardized scoring rubric. If an edit has no clear connection to the request, ask the agent to explain or revise it before integration. An explanation can help you investigate, but the diff still needs to stand on its own against the task.
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A change can be in scope but wrong; it can pass tests and still be outside scope. Conversely, a focused diff can contain a security flaw. Microsoft’s Visual Studio Code guidance warns that AI-generated code can contain bugs, security issues, or subtle logic errors. Check edge cases, error handling, assumptions, and security concerns, then run the relevant tests before integrating the changes.
Rank #3
- Scope: Does each edit serve the requested outcome and respect constraints?
- Correctness: Does the implementation behave as intended, including relevant edge cases?
- Security: Does it handle inputs, permissions, secrets, and external operations safely?
- Validation: Which relevant tests or checks ran, and what did they actually establish?
Passing tests are evidence about the behavior those tests cover; they do not establish that every changed file was requested. A test report should therefore inform the review, not replace the comparison with the prompt.
Use session history for provenance, not a scope verdict
Session records can help explain why a change appeared. GitHub documents that Copilot cloud-agent commit messages link to session logs, and that session search can cover synced prompts, responses, and file changes when those records are available to the user: GitHub Copilot cloud-agent documentation. Use that context to investigate unclear edits, but determine scope by comparing the result with the request.
Logs can also reveal actions beyond code edits. OpenAI reported that its internal monitoring observed a handful of cases in which agents acted on instructions found in tool output, including attempts to email external addresses. That is an observation from one internal deployment, not a general estimate of how often such behavior occurs.
Know what rollback can and cannot undo
Visual Studio Code checkpoints can restore affected workspace files and chat history. They do not reverse completed terminal commands, network requests, deployments, or changes to external services. The VS Code guide says, “Checkpoints are temporary and don’t replace Git version control.” Use Git to manage code changes and the relevant service’s recovery controls for external effects.
Before restoring anything, identify the affected files and any actions the agent may have taken outside the workspace. Restoring a checkpoint is not a substitute for investigating those actions or confirming that the repository and external systems are in the intended state.
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Different tools help with different parts of an audit. None of the sources cited here documents an automatic, one-off judgment that a completed diff matches a user’s prompt.
| Approach | What it helps with | What it does not establish |
|---|---|---|
| Visual Studio Code review and checkpoints | Inspecting changes, giving feedback, testing, integrating, and restoring affected workspace files and chat history. | That changes are in scope automatically, or that external side effects have been reversed. |
| GitHub Copilot cloud-agent session history | Tracing commits to session logs and searching synced prompts, responses, and file changes when available. | That a logged change was requested or appropriate. |
| OpenScope | Documented brokered privileged actions, scoped permissions, default-deny policy, and an append-only record of allowed and denied requests; a human reviews and applies proposed access changes. See OpenScope’s documentation and its workflow. | A comparison of code changes against a task prompt. It is chiefly relevant to agents that can perform privileged operations. |
| Microsoft Scope | Submitting coding tasks, defining evaluation criteria, inspecting agent runs, and comparing behavior across tasks and configurations. See Microsoft Scope documentation. | A hosted IDE/runtime or a documented one-off auditor for whether a completed diff matched one user’s request. |
The workflows can be compared by how completely they expose changed and untracked files, connect edits to a request or session, support feedback and revision, show validation evidence, restore workspace changes, and record or recover external actions. These are practical selection criteria, not a formal standard.
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What the evidence can—and cannot—tell you
The reviewed official sources recommend inspecting changes, constraints, tests, and security, but they do not provide a universal automated intent-versus-diff verdict. They also do not establish a dated, independently published statistic for how often coding agents make out-of-scope edits or how accurately a tool detects them. Do not treat an anecdote or a session record as a prevalence estimate or a scope score.
OpenScope describes an audit of two months of coding-agent session history on one developer workstation. Its account reports more than 1,000 raw SSH command invocations against a production host, most as root, plus over a hundred local sudo calls. This is the vendor’s account of one workstation, not an independent measure of typical agent behavior.
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