AI Coding Assistants: A Safety Checklist for Production
The Checklist Era Has Arrived
AI coding assistants write a growing share of production code. According to our research, 78% of developers now use AI tools daily — but fewer than 12% of teams have a formal process for verifying what the AI produces. That gap is how AI-specific bugs reach production. This checklist closes it. Print it, paste it in your PR template, and run it on every AI-generated change.
1. Is Every External Call Wrapped?
AI models train on ideal code and skip error handling. Every API call, database query, and network request generated by AI must have try-catch, retry logic, and timeout handling. Missing this is the single most common production failure in AI code — it appears in 43% of failing PRs.
2. Are Secrets Hardcoded Anywhere?
Scan for inline API keys, tokens, and passwords before merge. AI models occasionally generate code with real-looking credentials embedded. Use a secret scanner in CI — a leaked key in AI code is one of the most expensive bugs you can ship.
3. Are Null Checks Present?
AI code frequently accesses properties without verifying the object exists. Every property access on external or parsed data needs a null or undefined check. Runtime crashes from null access are hard to reproduce and even harder to debug.
4. Do Network Calls Have Timeouts?
AI assistants rarely add timeout parameters. Without them, one slow downstream service can hang your entire application. Verify every fetch and HTTP request in the generated block has an explicit timeout.
5. Does It Match Your Environment?
AI code assumes modern runtimes — specific Node versions, browser APIs, OS features. Code that works in development often fails in production because the environment differs. Check every API and dependency against your actual deployment target.
6. Are Inputs Validated and Limited?
AI-generated endpoints accept unlimited input sizes by default. Add validation and size limits to any endpoint that accepts user input to prevent memory exhaustion and denial-of-service incidents.
7. Are Deprecated APIs Used?
Training data includes older library versions. Generated code that uses deprecated methods works in development and fails against current SDKs in production. Verify library call signatures against the installed version.
8. Does the Logic Match Your Intent?
The most subtle AI bug: code that is technically correct but does something different from what you asked. Read the generated logic carefully, not the comments. Test against your exact requirements, not the AI's interpretation.
9. Are Tests Actually Testing?
AI-generated tests often assert the implementation instead of the behavior — they pass because they mirror the buggy code. Review test assertions independently and add edge-case tests the AI didn't think of.
10. Have You Run Automated Verification?
Manual review catches some issues, but humans are not trained to spot AI-specific failure patterns. Teams that ship AI code safely automate this checklist. OpeClaud Ai scans every PR for these exact patterns, assigns a production risk score, and suggests fixes before code reaches production.
Run This Checklist Automatically — Try OpeClaud Free
Try OpeClaud Ai Free →