“Vibe coding” becomes real engineering when the agent has to meet your engineering standards.
The gap between a quick demo and production-ready code isn’t the model’s capability. It’s the instructions.
Your agent doesn’t know your definition of “done” unless you tell it. A feature might look complete, but the tests, error handling, and deployment details are missing. Or it works locally but conflicts with your team’s conventions. Sound familiar?
Here’s the shift: treat your agent like a capable teammate who needs your playbook. The fastest way to improve AI-generated code isn’t to tweak the prompt endlessly—it’s to clarify the rules around it.
- Define the change and what must not change.
- Point the agent to current architecture and conventions.
- Require tests for new behavior and important edge cases.
- Run the same checks your team requires from people.
- Feed recurring review comments back into the instructions.
Speed matters, but less rework is the real win. A passing test suite doesn’t mean the agent added tests for the new behavior. A change that looks functional might not be maintainable. Human review isn’t a failure—it’s how you make the work reliable.
Coding standards shouldn’t live only in a reviewer’s head. If you expect an agent to follow them, write them down. And don’t skip your engineering process just because “the AI wrote it.”
Good code from an agent starts before it writes a line. Give it clear, enforceable instructions, and the output starts to look like your best engineers’ work.