Before an AI coding agent changes code, ask it to reproduce the failure and show what evidence points to the cause. Then have it rerun that same scenario, perform relevant checks, and inspect the diff. A plausible patch is a hypothesis—not proof that the bug was understood or fixed.
Why can an AI change code before proving what is broken?
An agent can make a change that looks reasonable without establishing that it addresses the reported failure. The first safeguard is to anchor the investigation to observable behavior: what steps trigger the issue, what result was expected, and what actually happened. OpenAI describes using application state, logs, metrics, and traces to reproduce and validate bugs in its own engineering workflow, while noting that those capabilities depend on the repository and its tooling (OpenAI’s account of harness engineering).
Evidence narrows a diagnosis; it does not automatically prove a root cause. A trace might show that an agent chose the wrong tool or received an error, for example, but that alone does not establish why an arbitrary application failed. Ask the agent to connect its suspected cause to a specific observation, such as a failing assertion, a relevant log entry, a trace step, or a difference in application state.
How to get an AI coding agent to reproduce a bug before fixing it
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Capture the failure
Record the steps and input that trigger the issue, the environment or build, the expected result, and the actual result. Save relevant output. If you need diagnostics from an agent session, configure capture before reproducing the problem: Visual Studio Code warns that debug-log capture is not retroactive. Its guidance then explains how to select a session and inspect its events and tool errors (VS Code’s agent-session debugging guide).
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Reproduce it before editing
Ask the agent to demonstrate the failure with a focused test or a minimal, repeatable sequence. Do not authorize a fix based only on a description of what might be wrong. OpenAI’s engineering account describes reproducing reported bugs before implementing fixes and validating the application afterward (OpenAI’s account of harness engineering).
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Request evidence for the diagnosis
Ask which observation supports the suspected cause and which step or assertion fails. OpenAI’s evaluation guidance recommends investigating traces to understand workflow behavior, then using datasets and evaluation runs when repeated assessment is needed (OpenAI’s agent-evaluation guide). A trace can help explain an agent workflow; it is not, by itself, proof of a root cause in application code.
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Keep the change focused
Ask for the smallest change that addresses the evidence and, where feasible, preserves the original failure as a regression check. Leave unrelated tests and behavior alone so the result remains interpretable. The appropriate test strategy depends on the bug; there is no single test type that suits every failure.
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Define the finish line and verify
Specify how success will be checked before the change is made. Afterward, rerun the original reproduction, run relevant existing checks, and inspect the diff. OpenAI’s Codex Goals guide recommends defining an outcome and a verification surface, such as a test, benchmark, report, artifact, or command output (OpenAI’s Codex Goals guide). Have the agent report the exact command or scenario it ran and the result.
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Report blockers honestly
If missing permissions, unavailable services or data, absent logs, or an intermittent failure prevents reproduction, ask the agent to say what it could not observe and what it checked instead. Separate observed facts from inference. A change that has not been verified against the failure should not be reported as a confirmed fix.
A prompt you can give the agent
Adapt this request to the bug and your repository:
Before editing, reproduce the reported failure. State the steps, input, environment, expected result, and actual result. Show the relevant failing test, log, trace, error, or application-state evidence, and explain what that evidence supports about the likely cause. If you cannot reproduce it, say what is missing and what you can verify instead; do not guess. Propose the smallest relevant change and a focused regression check. After editing, rerun the original reproduction and relevant checks, inspect the diff, and report the exact commands or scenarios and their results.
This makes the request concrete without assuming the agent has access to every log, service, or environment the issue may require. OpenAI describes its own end-to-end workflow in the context of a specific repository structure and toolchain; it should not be taken as a guarantee that the same observability is available in every project (OpenAI’s account of harness engineering).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What counts as useful verification?
Choose checks that exercise the reported behavior and make the expected result visible. Depending on the issue and project, that may mean:
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- A focused test that failed before the change and passes afterward.
- A repeatable manual sequence with the actual result recorded.
- A relevant log, trace, or state observation that confirms the failure path behaves as expected.
- Relevant existing checks, with their exact results and any failures reported.
- A diff review to see whether the change stayed within the intended scope.
These checks answer different questions. A passing test may verify a narrow case but not every affected path; a clean diff does not show that the behavior works. The useful report makes clear which checks ran and what each one established, rather than presenting a green result as broader proof than it is.
Further reading on 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.” The publisher page says the print book is planned for November 2026; availability may change (No Starch Press book page). It is optional further reading, not a substitute for reproducing and verifying the specific failure in front of you.
For broader study, Andreas Zeller’s author-maintained The Debugging Book presents automated software-debugging methods (The Debugging Book). Elsevier’s page for Zeller’s Why Programs Fail describes material on reproducing errors, testing, observation, and correcting defects (Elsevier’s book page).
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