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Fix AI-built app bugs by reproducing the failure, saving its exact error and context, identifying which layer failed, and making one targeted change at a time. Then rerun the original failing steps. A patch suggested or written by an AI assistant is not proof that the bug is fixed.
Start with evidence, not a new prompt
Before changing code, capture what happened. Record the steps that triggered the problem, what you expected, what the app did instead, the full error message, HTTP status if present, timestamp with timezone, and any request ID. For intermittent failures, save multiple examples. Remove API keys, tokens, passwords, and other secrets from logs or bug reports.
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Keep the reproduction as small as possible: the fewest actions that still produce the failure. This makes it easier to distinguish the broken path from code that is merely nearby.
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Find which layer is failing
An app may involve a browser or client, its own backend, an external API, a network route or proxy, and a deployed server. Work out how far the failing request travels before it stops. If the provider has no corresponding request, the fault may be in the client, a timeout, a proxy, or the network path—not the provider itself. For general API investigations, OpenAI recommends checking the relevant project, model, service tier, time range, HTTP requests, and error rates rather than relying on aggregate health data: Troubleshooting API Errors and Latency.
#1 Best Overall
For an app or plugin that fails after deployment, check separately whether the server is running, the endpoint responds, authorization succeeds, and required descriptors, resources, or bundled assets load. If streaming worked locally but breaks in production, inspect whether a reverse proxy, CDN, or load balancer buffers responses or supports server-sent events (SSE). OpenAI’s deployment guidance discusses these checks for apps and plugins: Production best practices.
Use the error to choose the next check
| Symptom | First checks | Evidence to preserve |
|---|---|---|
| 401 or authentication failure | Confirm the key or token is correct and active, formatted correctly, associated with the intended organization or project, and authorized for the requested action. | Exact error, status, affected project, and request ID if available. Do not expose the credential. |
| 400 or invalid request | Inspect required fields and the parameter named in the error. Compare the request with the documentation for that API method. | Request shape with secrets removed, parameter detail, status, and request ID. |
| 429 or rate limit | Read the error and any limit details; check for a Retry-After header and the SDK’s documented retry behavior. |
Timestamp, status, request ID, and retry information. |
| Timeout or no provider-side request | Check client timeout settings, network route, proxy, and whether the request left the app. | Client-side timestamp, timeout details, and any correlation or request ID. |
| App or plugin does not load | Check server state, endpoint, authorization, descriptor or resource, content security policy (CSP), and bundled assets. | Failed resource or endpoint, browser or server error, and deployment context. |
| Streaming stops after deployment | Inspect reverse-proxy, CDN, and load-balancer buffering and SSE handling. | Where the stream stops and the relevant server or proxy error. |
| Service errors or latency | Filter to the affected project, model, and service tier; examine HTTP errors and latency over the relevant time range. | Time range, timestamps, request IDs, and the filters used. |
OpenAI’s API error codes explain how to interpret API errors. Authentication failures and malformed requests are different problems: correct credentials and permissions for the former; correct the named field or request construction for the latter. Avoid repeatedly changing unrelated code.
Make one small change, then verify the original failure
- Choose a cause supported by the evidence. Use the status, error detail, and layer checks to narrow the problem before editing.
- Change one thing. A focused patch makes it easier to tell whether the suspected cause was real and reduces the chance of introducing a second defect.
- Repeat the same steps that failed. Check that the expected result now occurs, not just that the app builds or the assistant says the change is correct.
- Check nearby behavior. Exercise related paths that could be affected by the change, such as another request using the same credential or endpoint.
If the original failure still occurs, preserve the new error and revise the diagnosis rather than stacking speculative fixes. The evidence needed for framework-specific remedies depends on the actual stack and error; a React, database, authentication-provider, or hosting issue cannot be diagnosed reliably from the fact that AI helped build the app.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsRetry transient failures without creating a loop
A rate limit or temporary connection or service failure may be recoverable, but retries should be bounded and follow the API or SDK guidance. OpenAI says its official SDKs retry eligible rate-limit errors and honor Retry-After when present. Check the SDK behavior before adding another retry layer, and avoid unbounded loops that multiply requests while hiding the original failure. See Troubleshooting API rate limits and 429 errors.
For Agents API connection or service failures, inspect the status and saved state before retrying; follow the guidance for the specific failure rather than assuming every error is safe to repeat: Agents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What evidence says about bugs in AI coding tools
A 2026 study by its authors manually analyzed more than 3.8K publicly reported bugs in the open-source repositories of Claude Code, Codex, and Gemini CLI. The authors attributed 36.9% of those collected reports to API, integration, or configuration errors: 2026 study of reported bugs in AI coding tools.
Rank #4
That figure describes reports in those three coding tools. It is not an estimate of how often bugs occur in all apps built with AI, nor a ranking of bug types across AI-assisted applications. The practical takeaway is narrower: when an AI-related project fails, check interfaces, credentials, configuration, and deployment boundaries alongside the generated code itself.
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