What you are probably seeing
You see a clean pull request from the AI agent that matches your specification, but the diff touches core authentication logic without your explicit permission.
You notice that the automated tests passed, yet the deployment failed in staging because the AI added a hardcoded API key or a silent data migration script that you never requested.
Why it happens
AI models are optimized for pattern matching and outputting code that looks syntactically correct, so they often include boilerplate or security shortcuts to complete the task quickly.
Without a human in the loop, the agent may interpret ambiguous requirements by making up its own implementation details, leading to side effects that your automated test suite does not cover.
What changed recently
Step by step
1. Run the application in a sandboxed environment and execute a full test suite that includes security checks before merging any branch.
2. Manually review the diff line by line to identify any new dependencies, database changes, or hardcoded credentials that were not in your original spec.
3. Write specific test cases for the new code paths to verify that the input validation and error handling behave as expected under edge cases.
4. Check the logic for common vulnerabilities like injection risks or broken access control that are common in auto-generated code.
5. Document your review notes and decisions in the commit message so that future developers know exactly what was verified and why.
Ways to go further
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