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Your Agent Saved the File. Who Checked the Result?

An AI coding agent can save a change without proving it is correct. Review the diff, check the logic, and run relevant tests before accepting the work.
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The person who asked for the change—or the team responsible for accepting it—must check the result. A saved file shows that an agent wrote something; it does not prove the edit is correct, complete, or safe. Review the changed files, compare the diff with the request, and validate the behavior before keeping or integrating the work.

What a successful save does—and does not—tell you

A save confirms an action, not its outcome. An agent may write directly into a session folder or worktree, but the resulting change still needs review. Visual plausibility is not enough: an edit can miss part of the request, alter unrelated code, mishandle an edge case, or introduce a security problem.

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Visual Studio Code’s trust-and-safety guidance puts the distinction plainly: “Always review AI-generated code before committing.” The guidance recommends inspecting the diff before committing, merging, or opening a pull request.

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Who should check the agent’s work?

When you work alone, you are the immediate reviewer because you requested the change and decide whether to keep it. In a team, the usual code-review and integration process can provide another check. The exact workflow depends on the project, but the decision to accept the work should not be delegated to the fact that the agent reported a successful save.

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VS Code describes the user as directing the task and deciding which changes to keep. Its agent workflow also includes validation, not just asking and acting. The agent can help make changes and run checks; the human still reviews the files and the validation results.

How to verify a saved change

  1. Find the changed files and open the diff. In VS Code, review changes in the Chat view or Agents window, the diff editor, or Source Control. If work happened in an isolated session, inspect that session’s changes before applying or merging them. VS Code’s chat-sessions guidance explains the review and integration workflow.
  2. Compare each edit with the request. Ask whether the requested behavior is actually covered and whether the agent changed anything unrelated. VS Code recommends committing or stashing existing unrelated work first so the agent’s changes are easier to distinguish. That preparation makes the diff more useful as a review boundary.
  3. Inspect the logic, not only the lines that look changed. Check assumptions, edge cases, error handling, and any security-sensitive behavior relevant to the task. A small edit can affect a larger flow, so follow the changed code far enough to understand its consequences.
  4. Run relevant checks. Use tests that exercise the modified behavior, then use the debugger or other editor tools when test results alone do not settle the question. VS Code recommends testing AI-generated code and provides tools for inspecting and validating changes. Security guidance and agent-mode documentation describe these review and validation practices.
  5. Keep or integrate only when the evidence is satisfactory. Depending on the workflow, that may mean staging and committing a reviewed change, or applying or merging an isolated session’s work. A successful test run is useful evidence, but it does not replace checking that the change matches the task.

Why permissions and logs do not replace review

Approval settings, sandboxing, and workspace trust can constrain what an agent is allowed to do while it works. They answer a different question from a diff review: controls limit actions; reviewing the saved changes checks what actually resulted. An allowed edit can still be wrong.

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For organizations, activity logs can help reconstruct what an agent did, which tools it used, what approvals occurred, and what results came back. OpenAI describes its own security-triage agent as examining those kinds of logs, with analysis surfaced to its security team for review. That is an example of OpenAI’s internal process, not evidence that logs by themselves establish code correctness.

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Raise the bar before consequential use

The more consequential the result, the more carefully it needs to be validated. NERSC, a research-computing facility, tells its users to validate generated code, commands, configurations, and scientific results before production use or large resource allocations. That guidance is specific to NERSC’s environment; it is a practical example of why high-impact work warrants checks proportionate to its consequences, not a universal statement about legal responsibility.

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