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When AI-generated code compiles but fails at runtime, the build has proved only that the source is syntactically valid for the selected toolchain. It has not proved that the program calls real APIs, receives the right data, handles the required environment, or produces the intended result. Fix it by reproducing the exact failure, separating a thrown exception from an incorrect result, tracing runtime state with a debugger, checking AI-specific assumptions, making the smallest justified change, and proving the behavior with a regression test. A green build alone is not a repair.
What does “compiles but fails at runtime” actually mean?
Compilation and runtime correctness answer different questions. A compiler checks grammar, types and other language or project rules that it can determine before execution. Runtime diagnosis asks what happens when the program receives real inputs, loads configuration, calls a service, reads a file, follows a branch or handles an error.
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Start by putting the failure into one of two branches. The branch determines what evidence you need and which tools are useful.
The program throws an exception or exits
Keep the complete exception text, stack trace, exit code and the command or user action that triggered it. Do not copy only the last line of a trace. File names, line numbers, nested “caused by” exceptions and the call sequence often identify an earlier bad value or a missing precondition.
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The program runs but returns the wrong result
Write down a known expected result and the exact observed result for the same input. A wrong answer can be more dangerous than a crash because execution appears healthy. GitHub’s debugging guidance treats exceptions and incorrect output as separate failure modes; use an assertion or other oracle for the latter instead of waiting for an error message.
Why a successful build is weak evidence
AWS’s guidance on generated software makes the same distinction: code can build and pass linting while remaining functionally wrong. An AI assistant may also invent an API that resembles a real one, choose an argument shape that compiles but is rejected at runtime, or implement a plausible algorithm that violates a requirement. The compiler cannot validate those assumptions unless the project’s types and checks explicitly encode them.
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What should I record before changing any code?
Reproduce first. Capture a compact incident record so every later change can be compared with the original behavior.
- Trigger: the command, HTTP request, button sequence or scheduled job that starts the failing path.
- Input: the smallest real or synthetic input that still fails, including relevant files, payload fields and boundary values.
- Expected behavior: the result, side effect or error that the specification actually requires.
- Actual behavior: the full exception and trace, exit status, logs or incorrect output, copied without paraphrasing.
- Runtime context: language and runtime version, operating system, dependency lockfile state, working directory, environment variables (with secrets redacted), database or service versions, and whether the failure occurs locally, in a container, or after deployment.
- Frequency: whether it fails every time, only for a particular input, or intermittently.
Save this record in the issue or test description. If the failure is intermittent, add timestamps, request identifiers and correlation data. Never paste credentials, tokens or personal data into a debugging prompt or ticket.
How do I reproduce the same runtime setup?
A debugger is useful only when it launches the same entry point with equivalent arguments and prerequisites as the failing application. A passing run under a different setup is not evidence that the bug is gone.
- Identify the real entry point. Use the production command, service route, worker, script or test harness rather than a convenient file opened in an editor.
- Match the executable and dependencies. Confirm the language runtime, interpreter or virtual machine, package lockfile, native libraries and build mode. An AI-generated snippet may have been written for a different version of an API.
- Match the working directory and arguments. Relative paths, command-line flags and input encodings can change behavior even when the source is identical.
- Provide required configuration safely. Recreate non-secret feature flags, service endpoints, schemas and environment variables. Supply secrets through the normal secret store or local substitute; do not hard-code them for a reproduction.
- Recreate external dependencies. Use the same database schema, API contract, queue messages, file permissions and network assumptions where practical. If a dependency cannot be reproduced, record that limitation explicitly.
- Make the run repeatable. Put the input and setup in a script, fixture or test so another run starts from the same state.
Visual Studio Code’s documented launch.json configurations illustrate the settings that matter: program or entry point, arguments, environment variables, startup tasks and an attach target. You do not need that editor, but any debugger configuration should express the same facts. If the error appears only after deployment, state that local reproduction is not yet equivalent; a local build that passes does not clear the deployed configuration.
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How do I use a debugger to find the first wrong state?
Do not edit only the line named by the exception. The visible failure is often where an invalid value is finally used, not where it was created. Follow state backward and forward until an input, return value or branch first contradicts the intended behavior.
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- Set a breakpoint before the failing operation. Place it at the start of the handler, function or loop that processes the triggering input. Add a second breakpoint on the line where the exception is raised if necessary.
- Run the exact reproduction. Use the same arguments and environment recorded earlier. A different input can take a different branch and hide the defect.
- Inspect values before stepping. Check types, nullability, collection length, string encoding, units, timestamps, paths and object fields. Compare each value with the function’s documented preconditions.
- Step over and into calls deliberately. Step over trusted library code when you need to preserve context; step into application code or an adapter when a returned value first looks suspicious.
- Read the call stack. The stack shows how execution reached the failing line and which boundary supplied the bad value. Inspect callers, not just the current frame.
- Use the debug console or watch expressions. Evaluate safe, side-effect-free expressions that reveal lengths, keys, status codes and branch conditions. Avoid invoking functions that mutate production data.
- Mark the earliest violated assumption. Examples include a parser returning an empty object, an API returning a different field name, a path resolving outside the expected directory, or a retry branch receiving a non-retryable error.
Microsoft’s debugger documentation describes breakpoints, stepping, variable inspection, call stacks and a debug console as complementary views. Use all of them; a line number alone rarely explains why the value at that line is wrong.
Which AI-generated assumptions should I audit?
GitHub’s code-review guidance gives a useful checklist: “Look for hallucinated APIs, ignored constraints, or incorrect logic.” Apply it systematically rather than assuming the generated code is correct because its names look familiar.
API names and signatures
Open the installed library’s documentation or type definitions and confirm that every function, class, option and response field exists in the version your project uses. Check argument order, required headers, asynchronous behavior, return types and error semantics. A similarly named method may exist but perform a different operation.
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Dependency declarations and versions
Verify that each imported package is declared in the correct production dependency section, is present in the lockfile and is licensed for your use. Compare the generated import with the package’s documented module path. Remove a package that is unnecessary, abandoned or suspicious instead of accepting it merely because installation succeeds.
Data shapes and boundary cases
Trace data across serialization, network, database and UI boundaries. Confirm whether a field can be absent, whether numbers arrive as strings, whether a timestamp is in UTC, and whether an empty collection is valid. Test duplicate records, empty input, maximum sizes, malformed encoding, permission failures and timeouts when those cases are part of the contract.
Ignored requirements
Compare the implementation with the complete task, not just the last prompt. Generated code may omit authorization, transactional behavior, rate limits, cleanup, idempotency, accessibility, resource limits or a required output format. Write each non-negotiable constraint beside the relevant code path and check it explicitly.
Control flow and error handling
Look for conditions with reversed meaning, branches that always execute, broad exception handlers that hide the original error, retries without a limit, and cleanup that runs only on the success path. Verify that a caught error is either handled according to the contract or rethrown with its cause preserved.
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Tests that merely agree with the implementation
An AI-generated test can encode the same wrong assumption as the implementation. Check the expected value independently against the specification, a trusted fixture or a separately calculated example. A test that deletes the failing case, skips it, or asserts only that a function does not throw is not proof of correctness.
How do I make the smallest safe correction?
Once the first violated assumption is supported by runtime evidence, change only what is necessary to restore that contract. A narrow patch is easier to review and makes a regression test meaningful.
- State the defect in one sentence, such as “the adapter reads
userId, but this API returnsid.” - Choose the layer that owns the contract: input validation, adapter mapping, business rule, persistence, configuration or error handling.
- Add or preserve a test that fails for the original trigger and would pass only when the intended behavior is restored.
- Implement the smallest change that satisfies the contract. Avoid unrelated refactoring, dependency upgrades and formatting churn in the same patch.
- Review the diff for new permissions, logging of sensitive data, changed error exposure, resource leaks and compatibility effects.
If evidence points to an environmental mismatch rather than a code defect, fix the launch configuration or deployment setting and add a check that detects the mismatch early. Do not disguise a missing service, schema migration or secret as an algorithm change.
How do I verify that the fix is real?
Verification should move from the original failure to the surrounding system. A successful build is only one check in that sequence.
- Run the focused regression test. Use the exact failing input and assert the complete intended result, including important side effects or error classification.
- Run nearby tests. Exercise callers, adapters, related branches and boundary cases that could be affected by the change.
- Run static checks. Apply the project’s configured type checker, linter, security scanner and formatting checks. These can catch a new issue but cannot replace a behavioral assertion.
- Repeat the original command. Launch the application with the recorded entry point, arguments and environment. Confirm that the original exception or wrong output is gone.
- Check the deployment-equivalent path. If the bug occurred in a container, staging system or scheduled worker, run that same packaging and startup path. Document any dependency that remains different.
- Inspect logs and metrics. Confirm that the corrected path does not create new timeouts, retries, permission errors or data anomalies.
Microsoft documents targeted test execution and debugging as separate activities: use a focused test to iterate quickly, then run the broader suite. Never obtain a green result by deleting or skipping a failing test. Preserve the regression case in version control so a later generated change cannot silently reintroduce the defect.
How should I ask an AI assistant for debugging help?
Use an assistant to organize evidence and propose hypotheses, not to certify a rewrite. The quality of its suggestion depends on the quality of the reproduction you provide, and the suggestion still requires inspection and execution.
Include:
- the complete exception and stack trace, or the exact expected-versus-actual output;
- the smallest reproduction input and the command or action that starts it;
- the relevant function, its callers and the dependency or runtime versions;
- the intended contract, including constraints the code must preserve;
- what you already observed at a breakpoint; and
- which files or configuration are intentionally omitted, with secrets removed.
Ask for “two or three hypotheses, the evidence that would distinguish them, and a minimal check for each.” Then verify the proposed API against installed documentation, run the check yourself and review the diff. Do not accept a wholesale rewrite that removes the failing test, broadens permissions or changes behavior outside the reproduction.
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GitHub’s April 23, 2026 changelog describes Copilot web stack-trace analysis that uses repository context and reproduction steps. That is a vendor-described capability, not evidence that an assistant has found the cause in your program. Treat it as an additional reader of the trace.
What if the bug cannot be reproduced locally?
First classify the mismatch instead of editing blindly. Compare runtime versions, feature flags, data shape, clock and time zone, locale, filesystem permissions, network policy, concurrency and service responses between the failing and local environments.
- Configuration mismatch: export a sanitized configuration fingerprint and compare required keys, not secret values.
- Data mismatch: capture a privacy-safe fixture that preserves the failing shape, ordering and boundary values.
- Timing or concurrency issue: add request identifiers, structured timestamps and controlled retries; inspect for shared mutable state and race-prone cleanup.
- External-service issue: record status codes, response schemas and correlation IDs, then replay against a contract fixture where possible.
- Production-only failure: add targeted logging or tracing around the suspected invariant, with redaction and retention limits. Roll out a guarded fix or feature flag so behavior can be observed safely.
Until the environments are equivalent or the missing evidence is collected, describe the result as “not reproduced locally,” not “fixed.” A local build and test run cannot validate an unobserved production-only path.
Which diagnostic option gives the strongest evidence?
The best method depends on what you can observe and how much control you have over execution. Use this comparison to choose the next action.
| Situation | Evidence available | Control over execution | Verification quality |
|---|---|---|---|
| Thrown exception in a repeatable local run | Full trace, variables and call stack | High; debugger can pause and step | Focused regression assertion plus related tests |
| Wrong output in a repeatable local run | Expected result, observed result and intermediate values | High; breakpoints and watch expressions | Independent oracle and boundary-case tests |
| Deployment-only exception | Logs, trace, environment and request identifiers | Limited; use safe instrumentation or a staging replica | Deployment-equivalent replay, then broader tests |
| Intermittent or concurrency failure | Timestamped events, ordering and correlation data | Variable; controlled load or scheduling may help | Repeated deterministic test or a verified invariant |
| Build succeeds with no runtime evidence | Only compiler and static-check results | None until a trigger is defined | Insufficient; create a reproduction before changing code |
What common “fixes” make AI runtime bugs worse?
Changing the exception line without tracing inputs
Adding a null check or default value can hide the original contract violation and produce corrupted output later. Trace where the value became invalid, then decide whether rejection, conversion or a fallback is correct.
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Swapping one plausible method name for another without checking the installed version can turn a clear failure into a silent semantic error. Verify the signature and response contract first.
Catching everything
A broad catch block that logs “failed” and continues may convert an outage into incorrect data. Catch only errors the layer can handle, preserve causes and return the documented failure state.
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Disabling the failing test
Skipping a test removes evidence rather than repairing behavior. Keep the test, correct its independent expectation if necessary, and explain any temporary quarantine with an owner and a tracking issue.
Upgrading unrelated dependencies
A large upgrade makes it harder to identify the causal change and can introduce new runtime behavior. Pin the current versions while diagnosing; upgrade separately with its own tests and review.
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A 2026 study by researchers affiliated with the University of Missouri and SRI International reported that 68.3% of 300 generated projects executed out of the box under that paper’s clean-environment protocol. The other 31.7% did not. This is a result for that sample and protocol, not a universal failure rate for code that already compiles, so it cannot substitute for reproducing your own program.
Can a systematic debugging book help?
A structured reference can be useful when a team needs a shared vocabulary for reproduction, hypotheses, instrumentation and verification. Johannes Kuhlmann’s The Book of Debugging: A Systematic Workflow for Finding and Fixing Bugs is listed by No Starch Press with print ISBN 9781718504066 and a November 2026 preorder release when checked October 30, 2026. Confirm availability and listing details before purchasing; the workflow above does not require a book or a paid tool.
What is the repeatable workflow?
Use this sequence for every AI-generated runtime defect, whether it crashes or merely returns a wrong answer.
- Classify: exception/exit or incorrect output.
- Record: trigger, input, expected and actual behavior, complete evidence and environment.
- Reproduce: match entry point, arguments, runtime, dependencies, configuration and external services.
- Trace: break before the failure, inspect values and stack frames, and step to the earliest violated assumption.
- Audit: verify APIs, dependencies, data shapes, constraints, control flow and package trustworthiness.
- Patch narrowly: change the layer that owns the broken contract and preserve the original trigger as a test.
- Verify: run the focused test, related tests, static checks and the deployment-equivalent command.
- Document: record the root cause, evidence, environment limitation and the regression test so future AI edits can be reviewed against it.
This process turns an apparently mysterious AI failure into an evidence trail. The assistant can help summarize that trail or suggest checks, but the program’s runtime state and executable tests remain the authority.
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Why does AI code compile when the function it calls does not really exist?
The generated name may resolve to a symbol supplied by a different dependency, a mock, a dynamic lookup or a permissive type definition. Compilation proves only that the current project can type-check the reference; inspect the installed package and execute the exact call path to confirm its runtime behavior.
How can I prove that a wrong result is a bug rather than an unusual but valid output?
Define the contract independently of the implementation, using a specification, trusted fixture or separately calculated example. Then assert the relevant properties—such as totals, ordering, authorization or idempotency—rather than comparing the output only with another function generated from the same prompt.
What information should never be sent to an AI debugging assistant?
Remove passwords, access tokens, private keys, personal data, proprietary source that is not authorized for sharing and production records containing sensitive fields. Replace them with redacted fixtures that preserve types, relationships and boundary values, and verify the assistant’s data-retention policy before submitting context.
When should a runtime failure trigger a rollback instead of a code fix?
Rollback or disable the affected feature when the defect risks data loss, unauthorized access, repeated side effects or service instability and a safe patch is not yet verified. Preserve logs and the reproduction, then reintroduce a narrowly tested change under a controlled rollout.
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