Before an AI coding agent changes code, ask it to reproduce the failure and show what evidence supports its diagnosis. A patch is a hypothesis, not proof: confidence comes from rerunning the original scenario, checking relevant tests, and inspecting the diff. If the problem cannot be reproduced, the agent should say what it could verify—and what remains unknown.
Why did the AI change code before proving what was broken?
Because a plausible explanation can look like a diagnosis. An agent may see an error, infer a cause, and produce a change without demonstrating that the reported behavior is repeatable or that the suspected cause explains it. The result can appear reasonable while leaving the actual failure untouched.
OpenAI describes making bugs observable with application state, logs, metrics, traces, and isolated worktrees, then reproducing and validating fixes. That account also cautions that its end-to-end workflow depends on its own repository structure and tooling; it is not a guarantee that every project exposes the same evidence or supports the same reproduction method. OpenAI’s engineering account
There is no general rate established here for how often coding agents patch the wrong cause. The practical safeguard is not a promise that mistakes will disappear; it is a workflow that makes the failure, diagnosis, and result inspectable.
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How do you get an AI coding agent to reproduce a bug before fixing it?
Give the agent the observed behavior and ask it to demonstrate the failure before editing. Use a request like this, adapting the details to your project:
Do not edit code yet. First, reproduce this issue using the steps and environment below. Show the observed result versus the expected result, and identify the failing assertion, log entry, trace step, or state difference that supports your diagnosis. Then propose the smallest relevant change and a focused regression check. After editing, rerun the reproduction and relevant checks, inspect the diff, and report the exact commands or scenarios you ran and their results. If you cannot reproduce it, explain what evidence is missing and what you can verify instead.
Make the finish line observable. OpenAI’s Codex guidance recommends defining both an outcome and how to verify it—for example, with a test, benchmark, report, artifact, or command output. Codex Goals
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Use a failure-first debugging sequence
1. Capture what failed
Record the steps, input, environment or build, expected result, actual result, and relevant output. Keep enough detail for another run to follow the same path; “it broke” is not a useful reproduction description.
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2. Reproduce before editing
Ask for a minimal sequence or focused test that triggers the reported behavior. Compare what actually happens with what should happen. A repeatable failure anchors the investigation to the issue you reported, rather than to a nearby symptom or an assumption.
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If the failure is intermittent or depends on a service, data, browser state, permissions, or another environmental condition, have the agent state those dependencies. OpenAI’s engineering account describes reproducing reported bugs before implementing a fix and validating the changed application afterward. OpenAI’s engineering account
3. Ask what evidence supports the suspected cause
A diagnosis should point to an observation: a failing assertion, a particular log entry, a trace step, or a difference in application state. Ask which failed step and returned error matter, not just for a summary of what the agent thinks happened.
For agent workflows, a trace can help reveal what happened during tool use and handoffs. OpenAI’s evaluation guide suggests using traces to investigate workflow behavior, then datasets and evaluation runs when repeatability is needed. A trace of an agent’s actions is evidence about that workflow; by itself, it does not prove the root cause of arbitrary application code. OpenAI: Evaluation best practices
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4. Keep the change focused
Once the evidence supports a bounded hypothesis, ask for the smallest relevant change. Preserve the original failure as a regression check where feasible, and keep unrelated behavior and tests interpretable. A green test suite is less informative if tests were changed simply to make the new code pass.
There is no single test strategy that suits every bug. The useful principle is to match the check to the failure and expected behavior, rather than treating one passing command as universal proof.
5. Verify the original scenario and inspect the diff
After the change, rerun the reproduction where feasible, then run relevant existing checks. Ask the agent to state exactly which scenario or command it ran and what happened. Inspect the diff yourself for unrelated edits, weakened assertions, or changes broader than the diagnosis called for.
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Codex outputs can be checked through evidence such as citations, terminal logs, and test results, according to OpenAI’s Codex documentation. The same standard is useful across tools: look at the evidence, not only the agent’s claim that the task is complete.
6. Report blockers without claiming a fix
If the agent cannot reproduce the issue because logs are missing, a service is unavailable, permissions are restricted, or the behavior is intermittent, it should name the limit. It can still report what it observed and what checks it completed, but it should distinguish those facts from an inference about the cause. A change that looks plausible is not a verified fix without a relevant verification result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What counts as a useful verification result?
- Reproducibility: Can another run trigger the same failure using the documented steps?
- Evidence visibility: Can you inspect the relevant state, log, trace, error, or failing assertion?
- Verification strength: Does the check exercise the original failure and the expected behavior?
- Scope and risk: Is the patch bounded, with unrelated behavior and tests left interpretable?
- Environment fit: Could the agent access the services, data, logs, or browser state required for a meaningful check?
These are practical decision questions, not a published benchmark. A check is only as strong as its fit to the reported failure and the evidence it exposes.
Where to learn more about debugging
No Starch Press describes Johannes Kuhlmann’s The Book of Debugging: A Systematic Workflow for Finding and Fixing Bugs with the sequence “Reproduce, Probe, Examine, Fix.” Its publisher page says the print book is planned for November 2026; availability may change. No Starch Press: The Book of Debugging
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For a broader treatment, Andreas Zeller’s author-maintained The Debugging Book presents automated software-debugging methods. The publisher describes Why Programs Fail as covering reproducing errors, testing, observation, and correcting defects. Elsevier: Why Programs Fail
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