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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →An AI-built app working once proves only that one path succeeded in one environment at that moment. It does not prove the app can be rebuilt with the same dependencies, run in production, reach its external services, or survive later changes. To find the cause, capture the exact failure, locate the boundary where it occurs, inspect the relevant logs and configuration, then make one evidence-based change and rerun the same path.
Why a successful first run can be misleading
A preview or local run can succeed even when the project is not reproducible. The app may rely on an undeclared dependency, a local setting absent from deployment, or an external service that behaves differently under network delays or rate limits. A 2026 empirical study examined dependency gaps in projects generated by coding agents; its findings are evidence that reproducibility can be a problem, not proof that any particular app has a missing package. Read the study.
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Failures also do not always look like a blank page or a clear crash. An AI feature may return an unexpected result, or a model call may fail or time out while the rest of the app remains available. Microsoft’s production AI guidance recommends checking logs and whether dependencies are reachable, rather than relying only on what the interface shows. Microsoft’s production AI practices.
Diagnose the failure before changing code
- Capture the exact symptom. Note the affected screen or route, the action and input that triggered the problem, the time, and whether you were using a local run, preview, or production deployment. Copy the first error message and record whether the failure happens every time or intermittently. These details turn “it broke” into something another person—or a coding agent—can investigate.
- Find where the failure occurs. Decide whether the symptom appears in the browser interface, application server, database or another dependency, deployment startup, or an external API call. Inspect application and deployment logs around the failure time. For a model request, check whether it reached the provider and compare the matching time window.
- Compare the environments and prerequisites. Check the deployed app against the working local or preview version: configuration, required services, installed dependencies, authentication, and permissions. Microsoft’s guidance for recovering an agent from a failed task likewise recommends checking prerequisites and supplying the failed command and first error. Microsoft’s agent troubleshooting guide.
- For an API problem, check provider-side evidence. Open the provider’s request or error view, scope it to the affected project and model, and inspect status codes, error rates, and timing. If no corresponding request appears, investigate the client timeout, proxy, network, or load balancer; the failure may have happened before the provider received it. OpenAI’s API troubleshooting guidance describes using scoped health data to distinguish these cases. OpenAI API error and latency troubleshooting.
- Ask the coding agent to diagnose before editing. Give it the reproduction steps, exact command, first error, relevant logs, and the smallest affected area. Ask it to identify the evidence for its proposed cause and make a narrow change. A vague request to “fix the app” makes it harder to tell whether a change addressed the observed failure or introduced another one.
- Rerun the original path after a change. Repeat the same sequence that failed, then check the nearby flow most likely to share the affected code. Keep a known-good version available so you can compare or revert. Version control and reproducible artifacts are central to AWS guidance for hardening generative-AI applications. AWS guidance on preproduction hardening.
Which causes should you check?
Use the evidence to choose a branch; none of these possibilities is automatically the culprit, and the available sources do not establish a universal ranking of causes.
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- Dependency or runtime mismatch: A declared package list may omit something the app actually needs at runtime. Check whether the project can be installed and started again from its declared dependency state.
- Configuration or deployment prerequisites: Compare settings, required services, credentials, authentication, and permissions between environments. A value available on a developer’s machine may not exist in deployment.
- API, network, or timeout behavior: Check whether a request reached the provider. A missing provider-side event points toward the client or the network path; a recorded request and error narrows the investigation to the service response and client handling.
- Transient rate limits or server errors: Retries can help with temporary failures, but they cannot repair persistent logic or configuration problems. Google says its official Python Gemini SDKs use exponential backoff by default for transient timeouts, network issues, rate limits, and 5xx errors. Retry behavior depends on the SDK and its configuration. Google’s Gemini API troubleshooting guidance.
- Subtle AI behavior: If the app loads but its AI feature acts incorrectly, inspect model-call logs and dependency reachability as well as the visible result.
- Uncontrolled changes: If several edits or environment changes happened between the last working version and the failure, restore a known-good point and compare changes systematically. Versioning supports auditability and repeatability; it does not identify the faulty change on its own.
What one 2026 study does—and does not—show
The study titled AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Dependency Gaps in LLM-Based Coding Agents evaluated three coding agents across 300 projects using 100 standardized prompts. The authors reported an average of 15 minutes of manual debugging for projects that eventually ran. These are results from that study’s experiment, not a general failure rate, typical repair time, or prediction of how long your own app will take to fix. Read the paper.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the next failure easier to trace
Keep the steps that made the app run, its dependency declarations, configuration requirements, and changes under version control. When a bug appears, preserve the first error and the conditions that reproduce it before asking an agent to edit code. After a fix, rerun that same path. This creates a useful comparison: what failed, what changed, and whether the original behavior returned.
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