To find out whether an AI coding agent did more than you asked, compare its complete change set against the original task and constraints—not just whether the tests pass. Review scope, correctness, and security as separate questions. There is no universal automated prompt-to-diff score established by the available official guidance; a repeatable human review is the practical standard.
Keep the original request as your review standard
Before judging the code, identify the requested outcome, explicit limits, and acceptance checks. Constraints can include files or behavior that must remain untouched. Microsoft’s Visual Studio Code best-practices guidance 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 boundary concrete.
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If the request is vague, separate what it clearly required from assumptions the agent may have made. Treat an assumption as a reason to ask for clarification or inspect the change more closely, not as proof that the agent violated the task.
Inspect the complete change set
Look at every changed, added, and deleted file. Use the editor’s changes view, a unified diff, Source Control, or the pull request. A file list alone can reveal surprising additions, but the diff shows what actually changed inside each file.
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In Visual Studio Code, review the agent’s changes before committing or integrating them. The review guidance notes that Agent Host changes may already be saved in a folder or isolated worktree, so they may not be limited to unsaved editor buffers. See Microsoft’s review and revert agent changes documentation.
Decide whether each edit belongs
For each changed file and consequential edit, ask three questions:
- What part of the request does this support? Identify the requested behavior or acceptance check it serves.
- Is the change necessary? A related edit may still be broader than needed. Look for unrelated dependencies, configuration changes, endpoints, or behavior.
- Does it break a constraint? Compare the edit with explicit instructions about what must remain unchanged.
This is a practical review method, not a standardized scoring rubric published by the tools discussed here. A diff answers what changed; the task supplies the standard for deciding whether those changes were requested.
Review scope, correctness, and security independently
An in-scope change can still be wrong, and a passing test suite does not show that every changed file was requested. After the scope check, inspect edge cases, error handling, assumptions, and security implications. Microsoft’s Visual Studio Code documentation warns that “AI-generated code can contain bugs, security issues, or subtle logic errors.” Run relevant tests before integration, using the project’s established commands where possible.
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Also consider what the agent could do beyond editing files. 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 a specific observation from an internal deployment, not evidence of how often such incidents happen generally. If an agent can access tools or services, review those actions as well as its code.
Use session records to understand provenance
Logs can help explain why an agent made a change, but they do not determine whether it was authorized by the task. GitHub documents that Copilot cloud-agent commit messages link to session logs. Its session search can cover prompts, responses, and file changes when synced data is available to the user. See GitHub’s cloud-agent documentation.
Use a session record to trace a surprising edit back to the conversation or action that produced it. Then compare the edit with the request yourself; provenance is context, not proof of scope compliance.
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Several features can make oversight easier, but their purposes differ:
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| Approach | What it helps you inspect | What it does not establish |
|---|---|---|
| Visual Studio Code change review and checkpoints | Changed files and diffs; feedback and revision; restoration of affected workspace files and chat history. | Whether every edit matched the task. Checkpoints do not reverse completed terminal commands, network requests, deployments, or changes to external services. |
| GitHub Copilot session history | Links from cloud-agent commits to session logs and, when synced data is available, search across prompts, responses, and file changes. | An automatic judgment that the diff stayed within scope. |
| OpenScope | Privileged actions governed by named or parameter-scoped permissions, default-deny policy, and an append-only record of allowed and denied broker requests. Proposed access changes are reviewed and applied by a human. | A comparison of completed code changes with a user’s prompt. Its focus is privileged-action control and auditing. See the OpenScope documentation and workflow guide. |
| Microsoft Scope | Submitting coding tasks to agents, defining evaluation criteria, inspecting runs, and comparing behavior across tasks and configurations. | A hosted IDE or runtime, or a documented one-off auditor for whether a finished diff matched a particular user request. See Microsoft Scope’s documentation. |
Pick a workflow based on what you need to see: complete file changes, a link to the originating session, room for feedback and revision, visible validation evidence, workspace recovery, or logs of external actions. No dated, independently published statistic in the sources cited here measures how often coding agents make out-of-scope edits or how accurately a tool detects them.
Revert carefully, especially after external actions
Visual Studio Code checkpoints can restore affected workspace files and chat history. They are temporary and do not replace Git version control. Nor can they undo completed terminal commands, network requests, deployments, or changes made to external services. If the agent took one of those actions, use Git where appropriate and the relevant service’s recovery controls for external effects.
Before reverting, identify which files and actions need recovery. A workspace restore is not a rollback of everything the agent may have done.
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