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Verify the bug before you delegate the fix. First learn the project’s test framework and commands, run the relevant tests to capture a clean baseline, and define the expected behavior from requirements or documented behavior. Then add a focused reproduction test without changing implementation code. If that test fails for the reason described, you have evidence of a product defect; if it fails because setup or the expectation is wrong, fix the test or environment instead.
After an agent proposes a change, run the reproduction test again, run the related suite, and inspect the diff, assertions, skips, and actual command output. This sequence prevents an AI agent from “fixing” a symptom, encoding an existing bug into a test, or quietly changing the test until it passes.
Why verification comes before an AI-written fix
A bug report is a claim, not a diagnosis. A red test can indicate a defect in application code, but it can also come from a broken test setup or an incorrect expectation. Microsoft’s Visual Studio Code guidance makes the distinction explicit: “A failing test can reveal a bug, but it can also reflect faulty test setup or an incorrect expectation.” See Test existing code with AI.
AI coding agents are good at producing plausible changes quickly. That speed increases the cost of an unverified premise: an agent may alter working code to satisfy a mistaken assertion, add a brittle test for an unspecified behavior, or report success after editing the test rather than correcting the implementation. Verification gives the agent a fixed target and gives you evidence to review.
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The goal is not to distrust every report. It is to separate three questions that are often conflated:
- What does the project currently do?
- What should it do according to an independent requirement?
- Does a reproducible difference exist, and is the implementation the cause?
Only the third question justifies an implementation fix. A passing test suite or high coverage percentage is not, by itself, proof that the behavior is correct; correctness depends on the requirement and on tests that actually exercise the failure.
Start by learning the project’s testing rules
Before asking an agent to edit anything, ask it to inspect the repository and report how testing works. This reconnaissance should be read-only: no file edits, dependency installation, formatting changes, or “cleanup” while you are still establishing the baseline.
What the inspection should identify
- The test framework and runner (for example, a language-native runner, a JavaScript test runner, or a framework-specific harness).
- Where unit, integration, end-to-end, and fixture files live.
- Naming conventions, test discovery rules, setup and teardown hooks, mocks, snapshots, and environment variables.
- The documented command for one test or one file, and the command for the related suite.
- Required services, database fixtures, browsers, containers, feature flags, or platform assumptions.
- Contribution guidance that specifies supported runtime, dependency, or operating-system versions.
Ask the agent to quote the files and commands it used to reach each conclusion. If the project has no clear documentation, that is itself a risk to record; do not let the agent invent a new test structure merely to make the reported case run.
Use the narrowest command that proves the behavior
Every runner has a way to select one test, one class, or one file. Learn that selector before making changes. For example, a Python project might run a single file with pytest path/to/test_file.py, while a JavaScript project might pass a test path or name through its runner. These are patterns, not universal commands: use the exact command documented by the repository.
Also determine how the project signals skipped, expected-failure, quarantined, or flaky tests. A command that exits successfully while skipping the new test is not a reproduction, and a test marked as expected to fail does not establish a product defect.
Establish and record a baseline
Run the relevant existing tests before adding a reproduction. Preserve the command, commit or revision, environment details that matter, pass/fail/skip counts, and the complete failure output. If unrelated tests already fail, list them separately instead of treating them as evidence for the reported bug.
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A practical baseline record
| Record | Why it matters |
|---|---|
| Repository revision or branch | Lets reviewers distinguish your change from later code movement. |
| Runtime and dependency versions | Some failures occur only with a particular interpreter, compiler, library, or browser. |
| Exact targeted-test command | Provides a repeatable starting point for the reproduction and fix. |
| Exact related-suite command | Shows the intended regression boundary after the fix. |
| Passes, failures, skips, and expected failures | Prevents pre-existing red or silently skipped checks from being attributed to your change. |
| Relevant logs and environment settings | Captures service, locale, time-zone, feature-flag, or fixture conditions that can change results. |
Do not “repair” a pre-existing failure as part of baseline collection. If setup is broken, document that fact and decide whether it can be corrected independently. Otherwise an agent may claim that the bug is fixed when it only made the test environment runnable.
Define correctness independently of the current code
Write the expected behavior before looking for an assertion that mirrors the implementation. Use a product requirement, API contract, user-facing documentation, protocol specification, design decision, or an agreed bug-report clarification. Current code is evidence of what happens, not automatically evidence of what should happen.
Turn a report into an observable contract
State the input or precondition, the action, and the observable result. Include relevant boundaries and error behavior. For example:
- Given: an authenticated user with no remaining quota.
- When: the user submits one more request.
- Then: the API returns the documented status and error shape, performs no side effect, and records the specified audit event.
Do not add behavior that the requirement leaves unspecified. If ordering, wording, timing, rounding, or retry behavior is not defined, mark it as an open decision rather than choosing a value simply because the current implementation does.
Review edge cases before writing the test
- Minimum, maximum, empty, null, malformed, and duplicate inputs.
- First-use and repeated-use behavior, including idempotency.
- Permission, authentication, and tenant boundaries.
- Timeouts, retries, partial failures, and unavailable dependencies.
- Locale, time zone, daylight-saving, encoding, and numeric-precision effects.
- Concurrency or ordering when multiple operations can overlap.
Ask the agent to propose test cases and explain which requirement supports each expected value. Review that proposal before any test file is generated. An independent expected value is important: calculating the expected answer by calling the same function under test can reproduce its bug and still produce a green test.
Write a focused reproduction test first
Anthropic’s feedback-loop guidance states, “For bug fixes, write the failing test first.” Follow that order even when an agent is doing the typing. Add only the agreed reproduction test; leave production code unchanged.
Properties of a useful reproduction
- It exercises the smallest public behavior that demonstrates the report.
- Its setup is explicit, deterministic, and limited to necessary fixtures.
- Its assertions express the independent requirement, not incidental implementation details.
- Its failure message identifies the expected and actual observable values.
- It does not depend on network access, wall-clock timing, random data, or shared state unless those are the subject of the bug.
For a multi-layer report, begin at the narrowest layer that can prove the discrepancy. A unit test is preferable when the defect is pure logic; an integration or end-to-end test is appropriate when wiring, persistence, authentication, or browser behavior is essential. Do not replace a necessary integration test with a mock that hides the suspected failure.
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Protect the test from being rewritten
Commit the reproduction separately when your workflow allows it, or otherwise create a clear checkpoint. Tell the agent that the test and its expected values are immutable during the implementation step. Anthropic’s workflow recommends this separation so the agent cannot make the test pass by weakening or deleting its assertions.
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Run the reproduction and diagnose the red result
Now execute only the focused test. A failure is useful only when it matches the report and the reason you predicted. Capture the exact output, stack trace, logs, and exit status. Do not ask the agent to patch the implementation while the cause is still ambiguous.
| Observed result | Likely interpretation | Next action |
|---|---|---|
| Expected assertion fails with the reported actual value | Strong evidence of an implementation defect under the stated conditions. | Freeze the test and ask for a minimal implementation fix. |
| Fixture, import, service, or permission setup fails first | The environment prevented the behavior from being exercised. | Repair or document setup separately, then rerun the unchanged reproduction. |
| Actual behavior matches the requirement, but the assertion expects something else | The test expectation is wrong or the report is based on an outdated assumption. | Correct the requirement interpretation; do not change production code. |
| Test passes despite a credible report | The case may be environment-specific, nondeterministic, already fixed, or not represented by the test. | Compare versions and conditions, expand diagnostics, and reproduce under the reported scenario. |
| Test is skipped, marked expected-failure, or never collected | No evidence was obtained. | Fix discovery or policy issues and rerun; never count a skip as a pass. |
This diagnosis step is where many apparent AI “fixes” go wrong. A green result after changing setup, loosening an assertion, or skipping the case is not confirmation of a product correction. Compare the assertion with the agreed behavior and trace the failure to the first meaningful divergence.
Ask the agent to fix implementation code without changing the proof
Once the reproduction fails for the expected reason, give the agent a constrained implementation task. Include the requirement, the test command, the failure output, and boundaries such as files or APIs that must remain stable.
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- Keep the reproduction test and its expected values unchanged.
- Inspect the smallest relevant implementation path before proposing edits.
- Prefer a minimal change that addresses the documented cause rather than a broad refactor.
- Do not add dependencies, alter public behavior outside the requirement, or modify unrelated formatting.
- Explain any alternative behavior that the requirement does not specify instead of guessing.
- Run the focused test after the edit and show the complete command and output.
If the agent says the test is wrong, require a written comparison between the assertion and the cited requirement. A disagreement can be valid, but it must be resolved as a specification decision, not by silently editing whichever side is convenient.
Inspect the diff for test tampering
- Was the reproduction file changed after it was approved?
- Were expected values, assertion operators, or fixtures weakened?
- Were tests skipped, renamed, filtered, or marked as expected failures?
- Did the agent add a condition that avoids the failing input?
- Did the implementation change more files or code paths than the diagnosis requires?
Keep implementation and test changes easy to distinguish in version control. If both must change in one commit, inspect the patch line by line and record why each test edit is necessary.
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Verify in two passes: focused test, then related suite
Run the narrowest reproduction immediately after the fix. This gives fast feedback that the reported behavior changed. Then run the related unit, integration, or end-to-end suite identified during the initial inspection. The broader run checks regressions in neighboring behavior, while the focused run confirms the original claim.
Report execution, not just a green badge
For each command, report:
- The exact command and repository revision.
- Environment or service conditions that affect interpretation.
- Number of passed, failed, skipped, and expected-failure tests when the runner provides them.
- Warnings, retries, quarantines, and flaky behavior.
- Tests that could not run and the concrete reason.
- The relevant output, not only a summary copied from a dashboard.
OpenAI’s guidance for using Codex Cloud emphasizes reviewing actual run results and changes rather than relying on an agent’s completion message; see Using Codex Cloud. A tool may report that a command completed while tests were skipped, a service was unavailable, or a check never ran.
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Coverage can show that a line executed, but not that the assertion captures the required outcome. A high percentage can coexist with a test that accepts the wrong status, ignores an error field, or never checks a side effect. Treat coverage as supporting information after the requirement-based test and output have been reviewed.
Use a written acceptance checklist
A short checklist makes the handoff auditable and helps an agent maintain the same goal through several iterations. OpenAI’s documentation on defining goals in Codex describes using explicit goals to keep work aligned; see Using Goals in Codex.
- Requirement or authoritative behavior reference identified.
- Test framework, location, and commands inspected without edits or dependency installation.
- Relevant baseline run completed and pre-existing failures recorded.
- Focused test reviewed and expected values derived independently.
- Focused test failed before the implementation change for the expected reason.
- Reproduction test protected from edits during the fix.
- Implementation diff reviewed for scope and side effects.
- Focused test passed after the fix.
- Related suite completed, with skips and unrun checks disclosed.
- Final output and test diff retained for reviewers.
A prompt pattern that preserves the feedback loop
The following structure keeps investigation, reproduction, implementation, and verification separate. Adapt the terms to the project’s own vocabulary and put the requirement in the prompt rather than asking the agent to infer it from code.
Inspect the repository’s test framework, test locations, and documented commands. Do not edit files or install dependencies during this inspection. Report the command for one test and the related suite, plus any setup requirements.Run the relevant existing tests and record the exact output, pass/fail/skip counts, environment, and pre-existing failures.Requirement: [state the externally documented behavior]. Report: [state the input, action, and observed result]. Propose a focused test and explain why each expected value follows from the requirement. Do not edit implementation code yet.Add only the approved reproduction test. Keep implementation code and expected values unchanged. Run the focused test and report whether it fails for the stated reason. If setup or the expectation is wrong, stop and explain the diagnosis.After the reproduction is confirmed, fix the smallest relevant implementation path. Do not change the reproduction test, weaken assertions, skip tests, or add unrelated refactors. Run the focused test, then the related suite. Report exact commands, output, passes, failures, skips, unrun checks, and the final diff.
Replace bracketed text with the real requirement and report; the brackets are template markers, not values the agent should guess.
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The agent starts coding immediately
Stop the run and ask for a read-only inspection and baseline. Discard or isolate unreviewed edits, then return to the documented requirement. A patch written before reproduction is evidence-poor even if it appears reasonable.
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The reproduction fails during setup
Read the first failure in the trace, not the final assertion summary. Verify dependencies, fixtures, credentials, services, paths, and supported versions. Correct setup in a separate change or document that the environment cannot reproduce the report. Rerun the unchanged test before drawing a product conclusion.
The test passes before any fix
Check that the test was collected, that the reported input is actually used, and that the environment matches the report. Compare the current revision and configuration with the reporter’s. The issue may already be fixed, intermittent, or dependent on a missing condition.
The agent changes the test to make it green
Restore the approved test from the checkpoint, review the full diff, and rerun the pre-fix reproduction. Require an explanation for every test edit. If the requirement changed, update the requirement and test deliberately in a separate decision, not as a side effect of implementation.
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The suite is green but the bug remains
Inspect whether the targeted test asserts the externally visible failure, whether it ran in the relevant environment, and whether the suite skipped it. Add the missing boundary or integration condition only after confirming the requirement. Do not infer correctness from suite color alone.
The agent claims completion without usable evidence
Request exact commands, raw or linked logs, test counts, skipped and unrun checks, and the final diff. If those artifacts are unavailable, report verification as incomplete rather than converting the claim into a conclusion.
Security reports need the same discipline, with a specific caveat
OpenAI’s Codex Security documentation describes an analogous security workflow: “Validation is the step where Codex Security tries to reproduce a potential vulnerability in an isolated environment before surfacing it.” Reproducing a suspected vulnerability can reduce false positives and clarify impact, but this is product-specific security guidance, not a universal guarantee about every coding agent or every class of vulnerability.
For security-sensitive cases, isolate secrets and production data, use a disposable environment, and follow the project’s responsible-disclosure rules. A failed reproduction does not prove that a vulnerability is absent; it may mean the environment lacks the triggering condition. Record the exact assumptions and limitations alongside the result.
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What a trustworthy completion report looks like
A useful final report lets another engineer retrace the reasoning without asking the agent to repeat itself. It should identify the requirement, the pre-fix reproduction, the implementation change, and the post-fix evidence as separate items.
| Report section | Contents |
|---|---|
| Behavior | Requirement reference, inputs, expected result, and observed report. |
| Baseline | Revision, environment, commands, existing failures, and skips. |
| Reproduction | Test location, immutable assertion, pre-fix output, and diagnosis. |
| Fix | Files changed, causal explanation, and any explicitly accepted trade-off. |
| Verification | Focused and related-suite commands, counts, output, skips, and unrun checks. |
| Review notes | Diff inspection, remaining uncertainty, and follow-up work that is outside this fix. |
This format distinguishes “the command passed” from “the requirement was tested under the reported conditions.” It also makes it difficult for an accidental setup change or test edit to disappear inside an agent’s success message.
Bottom line: make the bug prove itself
Ask an AI coding agent to investigate first, reproduce second, and edit implementation code third. A focused test that fails for a requirement-backed reason is the proof that justifies a fix. Afterward, preserve that proof, run the targeted and related tests, inspect the diff, and disclose anything skipped or unrun. When the failure comes from setup or a mistaken expectation, correct that cause instead of forcing the product to match a bad test.
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